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Olimjan/uzbek_stt_v2	Olimjan	2025-05-26T10:02:31Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-04-21T14:11:01Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MaterialsInformaticsLaboratory/QA-SciBERT	MaterialsInformaticsLaboratory	2025-05-26T10:01:45Z	0	0	null	[ "pytorch", "tensorboard", "bert", "generated_from_trainer", "dataset:squad_v2", "region:us" ]	null	2025-05-26T10:00:43Z	--- tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: scibert-lr3-seed12-v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # scibert-lr3-seed12-v2 This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu116 - Datasets 2.3.2 - Tokenizers 0.12.1
Nerva1228/youbi01	Nerva1228	2025-05-26T10:01:16Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T10:01:15Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: youbi --- # Youbi01 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `youbi` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "youbi", "lora_weights": "https://huggingface.co/Nerva1228/youbi01/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Nerva1228/youbi01', weight_name='lora.safetensors') image = pipeline('youbi').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 5e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/youbi01/discussions) to add images that show off what you’ve made with this LoRA.
MaterialsInformaticsLaboratory/QA-MatSciBERT	MaterialsInformaticsLaboratory	2025-05-26T09:59:47Z	0	0	null	[ "pytorch", "tensorboard", "bert", "generated_from_trainer", "dataset:squad_v2", "region:us" ]	null	2025-05-26T09:58:34Z	--- tags: - generated_from_trainer datasets: - squad_v2 model-index: - name: matscibert-lr3-seed12 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # matscibert-lr3-seed12 This model is a fine-tuned version of [m3rg-iitd/matscibert](https://huggingface.co/m3rg-iitd/matscibert) on the squad_v2 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu116 - Datasets 2.3.2 - Tokenizers 0.12.1
Mekuu/LLAMA3.1-8b-Counsel-v1.5	Mekuu	2025-05-26T09:59:28Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T09:54:27Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: Mekuu - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Humberto21/Maria	Humberto21	2025-05-26T09:57:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T09:57:47Z	--- license: apache-2.0 ---
LioAlezard/ST4R20-00	LioAlezard	2025-05-26T09:55:09Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-26T08:31:05Z	--- license: other license_name: other license_link: LICENSE ---
LutaoJiang/DiMeR	LutaoJiang	2025-05-26T09:53:59Z	0	6	null	[ "Mesh", "Image-to-3D", "Text-to-3D", "3D", "image-to-3d", "dataset:allenai/objaverse", "arxiv:2504.17670", "license:apache-2.0", "region:us" ]	image-to-3d	2025-04-28T13:00:15Z	--- license: apache-2.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d tags: - Mesh - Image-to-3D - Text-to-3D - 3D --- # DiMeR: Disentangled Mesh Reconstruction Model Paper: https://arxiv.org/pdf/2504.17670 Project Page: https://lutao2021.github.io/DiMeR_page/ Github: https://github.com/lutao2021/DiMeR Space: https://huggingface.co/spaces/LTT/DiMeR ![teaser.png](https://cdn-uploads.huggingface.co/production/uploads/65d61d8fd484956b5acc89fe/-qx2PR8bfroUz3Gq3K2G7.png) # Abstract With the advent of large-scale 3D datasets, feed-forward 3D generative models, such as the Large Reconstruction Model (LRM), have gained significant attention and achieved remarkable success. However, we observe that RGB images often lead to conflicting training objectives and lack the necessary clarity for geometry reconstruction. In this paper, we revisit the inductive biases associated with mesh reconstruction and introduce DiMeR, a novel disentangled dual-stream feed-forward model for sparse-view mesh reconstruction. The key idea is to disentangle both the input and framework into geometry and texture parts, thereby reducing the training difficulty for each part according to the Principle of Occam's Razor. Given that normal maps are strictly consistent with geometry and accurately capture surface variations, we utilize normal maps as exclusive input for the geometry branch to reduce the complexity between the network's input and output. Moreover, we improve the mesh extraction algorithm to introduce 3D ground truth supervision. As for texture branch, we use RGB images as input to obtain the textured mesh. Overall, DiMeR demonstrates robust capabilities across various tasks, including sparse-view reconstruction, single-image-to-3D, and text-to-3D. Numerous experiments show that DiMeR significantly outperforms previous methods, achieving over 30% improvement in Chamfer Distance on the GSO and OmniObject3D dataset.
SSSHOGONE22/code-search-net-tokenizer	SSSHOGONE22	2025-05-26T09:53:15Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T08:49:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
dhchoi/manchu-llama32-11b-vision-mlx	dhchoi	2025-05-26T09:52:45Z	0	0	null	[ "safetensors", "mllama", "region:us" ]	null	2025-05-26T09:47:29Z	# Llama 3.2 11B Vision MLX This is a MLX-optimized version of the Llama 3.2 11B Vision model, converted for use with Apple Silicon devices. ## Model Description - Model Type: Vision-Language Model - Architecture: MllamaForConditionalGeneration - Parameters: 11B - Framework: MLX (Apple Silicon optimized) - Base Model: Llama 3.2 11B Vision ## Features - Optimized for Apple Silicon (M1/M2/M3/M4) devices - Vision and text understanding capabilities - Efficient inference with MLX framework - Support for image-text conversations ## Usage ```python import mlx.core as mx from mlx_vlm import load, generate # Load the model model, processor = load("path/to/model") # Generate text from image and prompt image_path = "your_image.jpg" prompt = "Describe this image" response = generate(model, processor, image_path, prompt) print(response) ``` ## Requirements - macOS with Apple Silicon - MLX framework - MLX-VLM package ## Installation ```bash pip install mlx mlx-vlm ``` ## Model Details - Vision Config: - Image size: 560x560 - Patch size: 14 - Hidden size: 1280 - Layers: 32 - Text Config: - Vocab size: 128,256 - Hidden size: 4096 - Layers: 40 - Attention heads: 32 ## License Please refer to the original Llama 3.2 license terms. ## Citation If you use this model, please cite the original Llama 3.2 paper and acknowledge the MLX conversion.
DngBack/TinyStories_Qwen3_4b_llm_head	DngBack	2025-05-26T09:52:31Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T09:52:07Z	--- license: apache-2.0 ---
AstroMLab/AstroSage-70B	AstroMLab	2025-05-26T09:52:21Z	15	3	null	[ "safetensors", "llama", "text-generation", "conversational", "en", "dataset:teknium/OpenHermes-2.5", "dataset:nvidia/Llama-Nemotron-Post-Training-Dataset", "arxiv:2505.17592", "arxiv:2411.09012", "arxiv:2407.04420", "base_model:meta-llama/Llama-3.1-70B", "base_model:finetune:meta-llama/Llama-3.1-70B", "license:llama3.1", "region:us" ]	text-generation	2025-05-18T23:32:31Z	--- license: llama3.1 datasets: - teknium/OpenHermes-2.5 - nvidia/Llama-Nemotron-Post-Training-Dataset language: - en base_model: - meta-llama/Llama-3.1-70B pipeline_tag: text-generation --- --- Model Name: AstroSage-70B Version: 1.0 Release Date: 2025-05-20 Developed by: AstroMLab (Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal, Tuan Dung Nguyen, Alberto Accomazzi, Emily Herron, Vanessa Lama, Azton Wells, Nesar Ramachandra, Rui Pan) Corresponding Contact: Tijmen de Haan ([email protected]) Funded by: * Oak Ridge Leadership Computing Facility (OLCF), a DOE Office of Science User Facility at Oak Ridge National Laboratory (U.S. Department of Energy). * Microsoft’s Accelerating Foundation Models Research (AFMR) program. * World Premier International Research Center Initiative (WPI), MEXT, Japan. * National Science Foundation (NSF). * UChicago Argonne LLC, Operator of Argonne National Laboratory (U.S. Department of Energy). License: Llama 3.1 Community License Reference Paper: Tijmen de Haan et al. (2025). "AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model" https://arxiv.org/abs/2505.17592 --- ### Model Details Model Type: Autoregressive transformer-based LLM, specialized in astronomy, astrophysics, space science, astroparticle physics, cosmology, and astronomical instrumentation. Base Model: Meta-Llama-3.1-70B Model Architecture: AstroSage-70B is a fine-tuned derivative of the Meta-Llama-3.1-70B architecture, making no architectural changes. The Llama-3.1-70B-Instruct tokenizer is also used without modification. Context Length: Fine-tuned on 8192-token sequences. Base model was trained to 128k context length. --- ### Model Description Overview: AstroSage-70B is a large-scale, domain-specialized language model tailored for research and education in astronomy, astrophysics, space science, cosmology, and astronomical instrumentation. It builds on the Llama-3.1-70B foundation model, enhanced through extensive continued pre-training (CPT) on a vast corpus of astronomical literature, further refined with supervised fine-tuning (SFT) on instruction-following datasets, and finally enhanced via parameter averaging (model merging) with other popular fine tunes. AstroSage-70B aims to achieve state-of-the-art performance on astronomy-specific tasks, providing researchers, students, and enthusiasts with an advanced AI assistant. This 70B parameter model represents a significant scaling up from the AstroSage-8B model. The primary enhancements from the AstroSage-8B model are: 1. Stronger base model, higher parameter count for increased capacity 3. Improved datasets 4. Improved learning hyperparameters 5. Reasoning capability (can be enabled or disabled at inference time) Training Lineage 1. Base Model: Meta-Llama-3.1-70B. 2. Continued Pre-Training (CPT): The base model underwent 2.5 epochs of CPT (168k GPU-hours) on a specialized astronomy corpus (details below, largely inherited from AstroSage-8B) to produce AstroSage-70B-CPT. This stage imbues domain-specific knowledge and language nuances. 3. Supervised Fine-Tuning (SFT): AstroSage-70B-CPT was then fine-tuned for 0.6 epochs (13k GPU-hours) using astronomy-relevant and general-purpose instruction-following datasets, resulting in AstroSage-70B-SFT. 4. Final Mixture: The released AstroSage-70B model is created via parameter averaging / model merging: * DARE-TIES with `rescale: true` and `lambda: 1.2` * AstroSage-70B-CPT designated as the "base model" * 70% AstroSage-70B-SFT (density 0.7) * 15% Llama-3.1-Nemotron-70B-Instruct (density 0.5) * 7.5% Llama-3.3-70B-Instruct (density 0.5) * 7.5% Llama-3.1-70B-Instruct (density 0.5) Intended Use: Like AstroSage-8B, this model can be used for a variety of LLM application, including * Providing factual information and explanations in astronomy, astrophysics, cosmology, and instrumentation. * Assisting with literature reviews and summarizing scientific papers. * Answering domain-specific questions with high accuracy. * Brainstorming research ideas and formulating hypotheses. * Assisting with programming tasks related to astronomical data analysis. * Serving as an educational tool for learning astronomical concepts. * Potentially forming the core of future agentic research assistants capable of more autonomous scientific tasks. We hope that with the enhanced intelligence and reasoning ability of AstroSage-70B compared to AstroSage-8B you can find additional use cases. --- ### Training Data AstroSage-70B's training data is split into pre-training (Continued Pre-Training, or CPT for short) and post-training (Supervised Fine-Tuning, or SFT). Continued Pre-Training (CPT) Data: The CPT data for AstroSage-70B starts with AstroSage-8B training dataset (see https://arxiv.org/abs/2411.09012 for more detail), and adds: * Data Processing & Cleaning: We apply `ftfy` post-processing. * Replay Data: We add a random selection of FineWeb samples to each training epoch. The added FineWeb samples are different for each epoch of training. Supervised Fine-Tuning (SFT) Data for `AstroSage-70B-SFT`: The SFT dataset is a diverse mix of astronomy-specific and general-purpose instruction-following data, totaling approximately 8.7 GB and over 7.5 million entries. The components are: * NVIDIA Reasoning, Science: 2048.0 MB (252,847 entries) * AstroSage Q&A: 1983.7 MB (4,683,569 entries) - Astronomy-specific question-answer pairs, see https://arxiv.org/abs/2411.09012 / https://doi.org/10.1038/s41598-025-97131-y * Teknium OpenHermes 2.5: 1621.0 MB (1,001,551 entries) * cosmosage Q&A: 616.0 MB (1,354,733 entries) - Cosmology-specific question-answer pairs, see https://arxiv.org/abs/2407.04420 / https://doi.org/10.1016/j.ascom.2025.100934 * NVIDIA Reasoning, Code: 600.0 MB (12,170 entries) * NVIDIA Reasoning, Math: 600.0 MB (33,236 entries) * NVIDIA Reasoning, Chat: 243.1 MB (36,395 entries) * Miscellaneous Other Astronomy & Instruction Data: 6 additional datasets totalling 810.2 MB (125,923 entries) --- ### Evaluation Quantitative evaluation using the AstroMLab-1 benchmark gives state-of-the-art performance, getting 86.2% of questions correct. This score is higher than all other models at the time of writing (May, 2025). ![Ting_et_al_2025](./mcq_score.png) --- ### Bias, Risks, and Limitations * Knowledge Cutoff: The model's knowledge cutoff is a mixture of the knowledge cutoff of the foundation model Llama-3.1-70B, which is December 2023, and the cutoff for the astro-ph papers used in the training dataset, which is January 2024. The model will not be aware of developments in astronomy after that cutoff. * Hallucinations & Factual Accuracy: All LLMs, including AstroSage-70B, can generate outputs that sound plausible but are incorrect or nonsensical. All scientific or factual claims must be verified by users. * Inherited Biases: The model may reflect biases present in its extensive training data (e.g., from scientific literature, general web data). * Errors in Training Data: After training completed, the AstroSage Q&A SFT dataset was found to have a non-trivial proportion of references to "the article" or "the text", which are artifacts of how the synthetic training data was generated. The insufficient cleaning resulted in AstroSage-70B inadvertently being trained to occasionally refer to information that is not available in the current conversation. We find that this failure mode is however, fairly rare. * Depth of Specialization: AstroSage-70B is highly specialized, having been trained on all astro-ph papers from April 2007 to January 2024. However, LLM training does not result in complete memorization of all facts. We find it excels in topics that are discussed in approximately five or more papers. --- ### Chat Template AstroSage-70B follows the Llama-3.1 chat template. For example: ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> You are an expert in astronomy, astrophysics, space science, cosmology, and astronomical instrumentation. Your role is provide helpful, factual answers to the user's query.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Explain the ISW effect.<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` --- ### Enabling Reasoning (a.k.a. Thinking, Chain-of-Thought) Like popular models such as o1, QwQ and DeepSeek-R1, AstroSage-70B is capable of reasoning through a problem before giving an answer. To enable this: 1. Set the system prompt to: `detailed thinking on` 2. Prefill the assistant response with `<think>` To enable reasoning for the example above, you would give ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> detailed thinking on<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> Explain the ISW effect.<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> <think> ```
otaku2015zero/phi4-mini-instruct-3b84-lora	otaku2015zero	2025-05-26T09:50:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-22T15:17:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWDlr	TanAlexanderlz	2025-05-26T09:47:41Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-26T08:40:03Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: ALL_RGBCROP_ori16F-8B16F-GACWDlr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ALL_RGBCROP_ori16F-8B16F-GACWDlr This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4175 - Accuracy: 0.8563 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 960 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6704 \| 0.05 \| 48 \| 0.6516 \| 0.6098 \| \| 0.5147 \| 1.05 \| 96 \| 0.5519 \| 0.6890 \| \| 0.318 \| 2.05 \| 144 \| 0.4741 \| 0.7561 \| \| 0.1843 \| 3.05 \| 192 \| 0.4970 \| 0.7988 \| \| 0.0684 \| 4.05 \| 240 \| 0.6018 \| 0.7866 \| \| 0.0323 \| 5.05 \| 288 \| 0.6950 \| 0.8293 \| \| 0.0124 \| 6.05 \| 336 \| 0.8211 \| 0.7927 \| \| 0.0023 \| 7.05 \| 384 \| 0.8972 \| 0.7927 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
outlookAi/UTIXxmftqQ	outlookAi	2025-05-26T09:47:24Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T09:31:11Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: mlegs --- # Utixxmftqq <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `mlegs` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "mlegs", "lora_weights": "https://huggingface.co/outlookAi/UTIXxmftqQ/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('outlookAi/UTIXxmftqQ', weight_name='lora.safetensors') image = pipeline('mlegs').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1500 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/outlookAi/UTIXxmftqQ/discussions) to add images that show off what you’ve made with this LoRA.
DorianVH/ppo-LunarLander-v2	DorianVH	2025-05-26T09:43:03Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-26T09:42:46Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 280.78 +/- 17.12 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jonliebrx/joninstaai	jonliebrx	2025-05-26T09:41:27Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T09:29:54Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Joninstaai <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/jonliebrx/joninstaai/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('jonliebrx/joninstaai', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/jonliebrx/joninstaai/discussions) to add images that show off what you’ve made with this LoRA.
tartuNLP/Llammas-base-AEG	tartuNLP	2025-05-26T09:40:45Z	11	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "base_model:tartuNLP/Llammas-base", "base_model:finetune:tartuNLP/Llammas-base", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-02T13:49:41Z	--- library_name: transformers base_model: - tartuNLP/Llammas-base --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
linoyts/Wan-VACE-14B-diffusers	linoyts	2025-05-26T09:40:11Z	25	1	diffusers	[ "diffusers", "safetensors", "video-to-video", "diffusers:WanVACEPipeline", "region:us" ]	null	2025-05-21T12:01:22Z	--- pipeline_tag: video-to-video library_name: diffusers ---
howaardharris/hmh	howaardharris	2025-05-26T09:39:27Z	0	0	null	[ "license:bsd-3-clause", "region:us" ]	null	2025-05-26T09:39:27Z	--- license: bsd-3-clause ---
Voidstep/gravel_qp68z	Voidstep	2025-05-26T09:37:44Z	0	0	null	[ "safetensors", "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-26T09:34:48Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
One-RL-to-See-Them-All/Orsta-32B-0321	One-RL-to-See-Them-All	2025-05-26T09:37:13Z	3	0	transformers	[ "transformers", "safetensors", "qwen2_5_vl", "image-text-to-text", "VLM", "multimodal", "conversational", "en", "dataset:One-RL-to-See-Them-All/Orsta-Data-47k", "arxiv:2505.18129", "base_model:Qwen/Qwen2.5-VL-32B-Instruct", "base_model:finetune:Qwen/Qwen2.5-VL-32B-Instruct", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-25T01:12:45Z	--- license: mit language: - en pipeline_tag: image-text-to-text tags: - VLM - multimodal library_name: transformers base_model: - Qwen/Qwen2.5-VL-32B-Instruct datasets: - One-RL-to-See-Them-All/Orsta-Data-47k --- # One RL to See Them All * 🐙 GitHub Repo: [MiniMax-AI/One-RL-to-See-Them-All](https://github.com/MiniMax-AI/One-RL-to-See-Them-All) * 📜 Paper (arXiv): [V-Triune: One RL to See Them All (arXiv:2505.18129)](https://arxiv.org/abs/2505.18129) * 💾 Dataset: [Orsta-Data-47k on Hugging Face](https://huggingface.co/datasets/One-RL-to-See-Them-All/Orsta-Data-47k) ## Model Overview Orsta-32B-0321 is a cutting-edge vision-language model (VLM) designed to achieve superior performance across a wide spectrum of both visual reasoning and visual perception tasks. This model is a result of post-training with [V-Triune](https://github.com/MiniMax-AI/One-RL-to-See-Them-All), our novel unified reinforcement learning (RL) system. The V-Triune system enables VLMs to be jointly optimized on diverse multimodal tasks within a single, cohesive training pipeline. Orsta-32B-0321 has been specifically trained using V-Triune on a carefully curated set of eight challenging visual tasks, fostering robust generalization and enhanced capabilities. ## Training with V-Triune Orsta-32B-0321's advanced abilities stem from its training with the V-Triune system. Key aspects of its training include: * Unified RL Framework (V-Triune): V-Triune is a Visual Triple-Unified Reinforcement Learning system featuring three core complementary components: * Sample-Level Data Formatting (to unify diverse task inputs) * Verifier-Level Reward Computation (to deliver custom rewards via specialized verifiers) * Source-Level Metric Monitoring (to diagnose problems at the data-source level) * It also incorporates an innovative Dynamic IoU reward mechanism, crucial for optimizing visual perception tasks. You can find more details in our paper: [V-Triune](https://arxiv.org/abs/2505.18129) * Diverse Joint Task Optimization: Orsta-32B-0321 was jointly optimized on the following eight visual tasks: * Visual Reasoning Tasks: Mathematics, Science Question Answering, Chart Understanding, and Puzzle Solving. * Visual Perception Tasks: Object Detection, Visual Grounding, Optical Character Recognition (OCR), and Object Counting. This comprehensive training allows Orsta-32B-0321 to develop a deeper understanding of visual content and its relation to textual prompts, excelling in tasks that require intricate reasoning and precise perception. ## Performance \| Model \| Knowledge \| Mathematics \| Perception \| Coding \| Info. Ex. \| Planning \| Science \| Metrics \| MEGA-Bench<br>Core \| \| :--------------------------------------------- \| ----------: \| ------------: \| -----------: \| -------: \| ----------: \| ---------: \| --------: \| --------: \| ------------------: \| \| QwenVL-2.5-32B-0321 \| 8.48 \| 12.62 \| 11.99 \| 13.59 \| 15.44 \| 8.61 \| 16.78 \| 14.91 \| 11.87 \| \| MM-Eureka-32B 💡 \| 12.20 \| 20.19 \| 21.88 \| 15.86 \| 21.23 \| 15.47 \| 19.95 \| 22.77 \| 18.57 \| \| VL-Rethinker-32B 💡 \| 12.16 \| 28.09 \| 22.99 \| 11.89 \| 21.50 \| 15.09 \| 28.10 \| 15.73 \| 19.41 \| \| Orsta-32B-0321 (Ours) 💡 \| 21.33 \| 28.55 \| 32.23 \| 19.44\| 26.38 \| 17.78 \| 33.20 \| 24.18 \| 25.94 \| \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| Δ (Ours - Backbone) \| +12.9 \| +15.9 \| +20.2 \| +5.9 \| +10.9 \| +9.2 \| +16.4 \| +9.3 \| +14.1 \| ## How to Use Orsta-32B-0321 is developed by post-training the [Qwen2.5-VL-32B-Instruct (0321 checkpoint)](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct/tree/98948557b47f3244ac2764806ddd334ce3c684f9) model using our V-Triune reinforcement learning system. The Qwen2.5-VL-32B-Instruct (0321 checkpoint) is a publicly available baseline known for its reliable core reasoning abilities, alongside certain recognized limitations in perception and output formatting (which have been addressed in subsequent Qwen releases). Applying V-Triune to this specific baseline demonstrates its powerful post-training capability to unlock the model's inherent potential and significantly elevate its performance by refining and amplifying existing strengths. Consequently, the core usage of Orsta-32B-0321, particularly regarding input formatting and model interaction, largely follows the established patterns of the Qwen2.5-VL series. Users familiar with Qwen2.5-VL models should find the interface intuitive. For comprehensive details on the general capabilities of Qwen2.5-VL models, including multi-turn dialogue format and image input specifics, we recommend referring to the official [Qwen2.5-VL series documentation](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct) (please ensure to consult information relevant to the 32B Instruct version). ## Citation 🏆 If you use Orsta-32B-0321 or the V-Triune system in your research, please cite our work: ```bibtex @article{ma2025one, title={One RL to See Them All: Visual Triple Unified Reinforcement Learning}, author={Ma, Yan and Du, Linge and Shen, Xuyang and Chen, Shaoxiang and Li, Pengfei and Ren, Qibing and Ma, Lizhuang and Dai, Yuchao and Liu, Pengfei and Yan, Junjie}, journal={arXiv preprint arXiv:2505.18129}, year={2025} } ```
tartuNLP/nllb1.3-smugri4-v0.01	tartuNLP	2025-05-26T09:36:55Z	14	3	transformers	[ "transformers", "safetensors", "m2m_100", "text2text-generation", "nllb", "translation", "fkv", "izh", "kca", "koi", "kpv", "krl", "liv", "lud", "mdf", "mhr", "mns", "mrj", "myv", "olo", "sjd", "sje", "sju", "sma", "sme", "smj", "smn", "sms", "udm", "vep", "vot", "vro", "deu", "eng", "est", "fin", "hun", "lvs", "nor", "rus", "base_model:facebook/nllb-200-1.3B", "base_model:finetune:facebook/nllb-200-1.3B", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-03-25T10:52:21Z	--- language: - fkv - izh - kca - koi - kpv - krl - liv - lud - mdf - mhr - mns - mrj - myv - olo - sjd - sje - sju - sma - sme - smj - smn - sms - udm - vep - vot - vro - deu - eng - est - fin - hun - lvs - nor - rus language_details: >- fkv_Latn, izh_Latn, krl_Latn, liv_Latn, lud_Latn, olo_Latn, sje_Latn, sju_Latn, sma_Latn, sme_Latn, smj_Latn, smn_Latn, sms_Latn, vep_Latn, vot_Latn, vro_Latn, kca_Cyrl, koi_Cyrl, kpv_Cyrl, mdf_Cyrl, mhr_Cyrl, mns_Cyrl, mrj_Cyrl, myv_Cyrl, sjd_Cyrl, udm_Cyrl, eng_Latn est_Latn, fin_Latn, hun_Latn, lvs_Latn, nor_Latn, rus_Cyrl library_name: transformers tags: - nllb - transformers pipeline_tag: translation license: cc-by-4.0 base_model: - facebook/nllb-200-1.3B --- # Smugri-tuned NLLB-1.3b, v0.01 This is a fine-tune of NLLB-1.3b with parallel data for 29 Finno-Ugric languages. It supports different dialect/variety generation for some of the languages, more info below. Info on used data and other details: soon. The training of this model is in progress, there are several known problems and overall quality is not tested yet. So far only parallel data was taken into training, more dialects are to come after monolingual/synthetic data is added. Usage in Python, to translate from English to Veps (New written Veps dialect/variety): ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model = AutoModelForSeq2SeqLM.from_pretrained("tartuNLP/nllb1.3-smugri4-v0.01") tokenizer = AutoTokenizer.from_pretrained("tartuNLP/nllb1.3-smugri4-v0.01") input_text = "<New written Veps> This is a short example sentence." source_lang = "eng_Latn" target_lang = "vep_Latn" tokenizer.src_lang = source_lang input_tokenized = tokenizer(input_text, return_tensors="pt") output_raw = model.generate(*input_tokenized, forced_bos_token_id=tokenizer.convert_tokens_to_ids(target_lang)) output = tokenizer.decode(output_raw[0], skip_special_tokens=True) print(output) # should be 'Nece om lühüd ozutezsana.' # for '<Central Eastern Veps>' the output becomes 'Nece om lühüd naverz’ sanond.' ``` ## Supported languages - `est_Latn` (Estonian), `fin_Latn` (Finnish), `fkv_Latn` (Kven), `izh_Latn` (Izhorian), `krl_Latn` (Proper Karelian), `liv_Latn` (Livonian), `lud_Latn` (Ludian), `olo_Latn` (Livvi-Karelian), `vep_Latn` (Veps), `vot_Latn` (Votic), `vro_Latn` (Võro) - `sje_Latn` (Pite Sami), `sju_Latn` (Ume Sami), `sma_Latn` (Southern Sami), `sme_Latn` (Northern Sami), `smj_Latn` (Lule Sami), `smn_Latn` (Inari Sami), `sms_Latn` (Skolt Sami), `sjd_Cyrl` (Kildin Sami) - `kpv_Cyrl` (Komi-Zyrian), `koi_Cyrl` (Komi-Permyak), `udm_Cyrl` (Udmurt) - `mdf_Cyrl` (Moksha), `myv_Cyrl` (Erzya) - `mhr_Cyrl` (Meadow Mari), `mrj_Cyrl` (Hill Mari) - `hun_Latn` (Hungarian), `kca_Cyrl` (Khanty*), `mns_Cyrl` (Mansi) - `eng_Latn` (English), `lvs_Latn` (Latvian), `rus_Cyrl` (Russian), `nor_Latn` (Norwegian) ## Supported dialects - for Izhorian: `alal` (Lower Luga), `soik` (Soikkola) - for Votic: `I`, `J`, `Ja`, `K`, `Kõ`, `Ke`, `Ko`, `L`, `Li`, `Lu`, `M`, `P`, `Po`, `R`, `Ra`, `S`, `U`, `V` (explanation: https://arhiiv.eki.ee/dict/vadja/lisad/v_lyhendid.pdf) - for Karelian Proper: `Dyorzha`, `Ilomantsi`, `Keret`, `Kestenga`, `Kontokki`, `Korbiselga`, `Maslozero`, `Myandyselga`, `New written Tver`, `New written karelian`, `Oulanga`, `Padany`, `Panozero`, `Poduzhemye`, `Porosozero`, `Reboly`, `Rugozero`, `Suistamo`, `Suoyarvi`, `Tikhtozero`, `Tikhvin`, `Tolmachi`, `Tunguda`, `Uhta`, `Valdai`, `Vesyegonsk`, `Voknavolok`, `Vychetaibola`, `Yushkozero` - for Ludian: `Central Ludian (Munozero)`, `Mikhailovskoye`, `New written Ludian`, `Northern Ludian (Kondopoga)`, `Southern Ludian (Svjatozero)`, `Miikul` (Central Ludian) - for Livvi-Karelian: `Impilahti`, `Kondushi`, `Kotkozero`, `Nekkula`, `New written Livvic`, `Rypushkalitsa`, `Salmi`, `Suoyarvi`, `Syamozero`, `Tulmozero`, `Vedlozero`, `Vidlitsa` - for Veps: `Central Eastern Veps`, `Central Western Veps`, `New written Veps`, `Northern Veps`, `Southern Veps` - for Kildin Sami: `orth1` - for Khanty: `kazym` (Kazym), `shuryshkary` (Shuryshkar)
Nerva1228/youbi	Nerva1228	2025-05-26T09:36:25Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T09:36:23Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: youbi --- # Youbi <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `youbi` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "youbi", "lora_weights": "https://huggingface.co/Nerva1228/youbi/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Nerva1228/youbi', weight_name='lora.safetensors') image = pipeline('youbi').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 5e-05 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/youbi/discussions) to add images that show off what you’ve made with this LoRA.
tartuNLP/xls-r-300m-et-children	tartuNLP	2025-05-26T09:35:51Z	19	0	transformers	[ "transformers", "pytorch", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "et", "base_model:TalTechNLP/xls-r-300m-et", "base_model:finetune:TalTechNLP/xls-r-300m-et", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2023-01-30T11:44:55Z	--- license: cc-by-4.0 tags: - generated_from_trainer metrics: - wer model-index: - name: xls-r-300m-et-children results: [] language: - et library_name: transformers base_model: - TalTechNLP/xls-r-300m-et --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xls-r-300m-et-children This model is a fine-tuned version of [TalTechNLP/xls-r-300m-et](https://huggingface.co/TalTechNLP/xls-r-300m-et) on an Estonian children's speech dataset. More information about the model's performance and the data used for evaluation and training: Luhtaru, Agnes; Jaaska, Rauno; Kruusamäe, Karl; Fishel, Mark (2023). Automatic Transcription for Estonian Children’s Speech. In: Proceedings of the 24th Nordic Conference on Computational Linguistics. [https://openreview.net/forum?id=xbPTfBIUby](https://openreview.net/forum?id=xbPTfBIUby) ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 200 - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.3485 \| 1.61 \| 100 \| 0.2034 \| 0.1782 \| \| 0.1914 \| 3.22 \| 200 \| 0.1818 \| 0.1606 \| \| 0.1641 \| 4.83 \| 300 \| 0.1770 \| 0.1572 \| \| 0.1479 \| 6.45 \| 400 \| 0.1745 \| 0.1546 \| \| 0.1374 \| 8.06 \| 500 \| 0.1751 \| 0.1538 \| \| 0.1244 \| 9.67 \| 600 \| 0.1734 \| 0.1518 \| \| 0.1211 \| 11.29 \| 700 \| 0.1753 \| 0.1508 \| \| 0.1096 \| 12.9 \| 800 \| 0.1758 \| 0.1483 \| \| 0.1059 \| 14.51 \| 900 \| 0.1771 \| 0.1469 \| \| 0.0991 \| 16.13 \| 1000 \| 0.1776 \| 0.1469 \| \| 0.0965 \| 17.74 \| 1100 \| 0.1759 \| 0.1469 \| \| 0.0944 \| 19.35 \| 1200 \| 0.1784 \| 0.1459 \| \| 0.0902 \| 20.96 \| 1300 \| 0.1799 \| 0.1469 \| \| 0.0867 \| 22.58 \| 1400 \| 0.1814 \| 0.1440 \| \| 0.0817 \| 24.19 \| 1500 \| 0.1828 \| 0.1438 \| \| 0.0802 \| 25.8 \| 1600 \| 0.1845 \| 0.1438 \| \| 0.0762 \| 27.42 \| 1700 \| 0.1843 \| 0.1431 \| \| 0.0774 \| 29.03 \| 1800 \| 0.1839 \| 0.1432 \| \| 0.0741 \| 30.64 \| 1900 \| 0.1843 \| 0.1442 \| \| 0.0718 \| 32.26 \| 2000 \| 0.1846 \| 0.1429 \| \| 0.07 \| 33.86 \| 2100 \| 0.1852 \| 0.1429 \| \| 0.0692 \| 35.48 \| 2200 \| 0.1872 \| 0.1435 \| \| 0.0671 \| 37.1 \| 2300 \| 0.1874 \| 0.1433 \| \| 0.0667 \| 38.7 \| 2400 \| 0.1887 \| 0.1435 \| \| 0.066 \| 40.32 \| 2500 \| 0.1880 \| 0.1422 \| \| 0.0628 \| 41.93 \| 2600 \| 0.1897 \| 0.1426 \| \| 0.0643 \| 43.54 \| 2700 \| 0.1910 \| 0.1428 \| \| 0.0643 \| 45.16 \| 2800 \| 0.1900 \| 0.1431 \| \| 0.0638 \| 46.77 \| 2900 \| 0.1900 \| 0.1427 \| \| 0.0601 \| 48.38 \| 3000 \| 0.1911 \| 0.1431 \| \| 0.0593 \| 49.99 \| 3100 \| 0.1914 \| 0.1432 \| \| 0.0606 \| 51.61 \| 3200 \| 0.1912 \| 0.1433 \| \| 0.0609 \| 53.22 \| 3300 \| 0.1912 \| 0.1431 \| \| 0.0587 \| 54.83 \| 3400 \| 0.1921 \| 0.1429 \| \| 0.0567 \| 56.45 \| 3500 \| 0.1924 \| 0.1430 \| \| 0.0611 \| 58.06 \| 3600 \| 0.1927 \| 0.1431 \| \| 0.0581 \| 59.67 \| 3700 \| 0.1926 \| 0.1430 \| ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.12.1+rocm5.1.1 - Datasets 2.7.1.dev0 - Tokenizers 0.13.2
quickstep3621/dippy-v11-1-5	quickstep3621	2025-05-26T09:33:18Z	3	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T09:33:13Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
tartuNLP/Llammas-base	tartuNLP	2025-05-26T09:32:29Z	60	1	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "et", "en", "arxiv:2404.04042", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-16T08:45:07Z	--- language: - et - en pipeline_tag: text-generation base_model: - meta-llama/Llama-2-7b-hf --- # LLammas-base 🐑 Llama-2-7B with continued pre-training of 5B tokens of CulturaX (75% Estonian, 25% English documents). This model is also instruction-tuned resulting in [Llammas](https://huggingface.co/tartuNLP/Llammas). More details in our [paper](https://arxiv.org/abs/2404.04042). ### Citation ``` @misc{kuulmets2024teaching, title={Teaching Llama a New Language Through Cross-Lingual Knowledge Transfer}, author={Hele-Andra Kuulmets and Taido Purason and Agnes Luhtaru and Mark Fishel}, year={2024}, eprint={2404.04042}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
bansarivyas3/nbhgty	bansarivyas3	2025-05-26T09:30:29Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-05-26T09:30:27Z	--- license: creativeml-openrail-m ---
RajeevanL/distilled_XLMRoberta_153_v5	RajeevanL	2025-05-26T09:30:01Z	0	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	question-answering	2025-05-26T09:29:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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memengoc/chatgpt3.5nn	memengoc	2025-05-26T09:29:52Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openchat/openchat-3.5-0106", "base_model:adapter:openchat/openchat-3.5-0106", "region:us" ]	null	2025-05-26T09:26:21Z	--- base_model: openchat/openchat-3.5-0106 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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JavaneseHonorifics/Unggah-Ungguh-Javanese-GPT2-Classifier	JavaneseHonorifics	2025-05-26T09:29:00Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-classification", "jv", "dataset:JavaneseHonorifics/Unggah-Ungguh", "arxiv:2502.20864", "base_model:w11wo/javanese-gpt2-small-imdb", "base_model:finetune:w11wo/javanese-gpt2-small-imdb", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T09:20:29Z	--- license: cc-by-nc-4.0 language: - jv datasets: - JavaneseHonorifics/Unggah-Ungguh base_model: - w11wo/javanese-gpt2-small-imdb pipeline_tag: text-classification library_name: transformers --- # Unggah-Ungguh-Javanese-GPT2-Classifier Unggah-Ungguh-Javanese-GPT2-Classifier is part of the Unggah-Ungguh's model family, a classifier model for Javanese Honorific Classification task that was mentioned in "Do Language Models Understand Honorific Systems in Javanese?". Check out [our paper](https://arxiv.org/abs/2502.20864) for more information! ## Model description - Model type: A classifier model trained on a highly curated Unggah-Ungguh dataset that represent Javanese Honorific rules and systems. - Language(s) NLP: Javanese - License: CC-BY-NC 4.0 - Finetuned from model: w11wo/javanese-distilbert-small-imdb ## Model Sources - Project Page: https://javanesehonorifics.github.io/ - Repository: https://github.com/JavaneseHonorifics - Paper: https://arxiv.org/abs/2502.20864 ## Using the model ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch model_path = "JavaneseHonorifics/Unggah-Ungguh-Javanese-GPT2-Classifier" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForSequenceClassification.from_pretrained(model_path) INPUT_TEXT = "Mbak Srini mangan pecel ajange pincuk" tokenized_input = tokenizer([INPUT_TEXT], return_tensors="pt", truncation=True, padding=True) with torch.no_grad(): outputs = model(**tokenized_input) y_pred = outputs.logits.argmax(-1) print("Predicted class:", y_pred.item()) ``` ## License and Use Unggah-Ungguh is licensed under the CC-BY-NC 4.0 ## Citation ```bibtex @article{farhansyah2025language, title={Do Language Models Understand Honorific Systems in Javanese?}, author={Farhansyah, Mohammad Rifqi and Darmawan, Iwan and Kusumawardhana, Adryan and Winata, Genta Indra and Aji, Alham Fikri and Wijaya, Derry Tanti}, journal={arXiv preprint arXiv:2502.20864}, year={2025} } ```
akashmadisetty/fine-tuned-translation-qwen	akashmadisetty	2025-05-26T09:25:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen3", "trl", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-26T09:25:29Z	--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: akashmadisetty - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-1.7B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
primeline/whisper-tiny-german-1224	primeline	2025-05-26T09:25:17Z	589	9	transformers	[ "transformers", "onnx", "safetensors", "whisper", "automatic-speech-recognition", "de", "arxiv:2409.03137", "doi:10.57967/hf/3826", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-23T18:20:51Z	--- license: apache-2.0 language: - de library_name: transformers pipeline_tag: automatic-speech-recognition --- ### Summary This model card provides information about a model based on the tiny whisper architecture that has been trained for speech recognition in German. Whisper is a powerful speech recognition platform developed by OpenAI. ### Applications This model can be used in various application areas, including - Transcription of spoken German language - Voice commands and voice control - Automatic subtitling for German videos - Voice-based search queries in German - Dictation functions in word processing programs ## Evaluations - Word error rate ``` +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| Model \| All \| Tuda-De \| multilingual librispeech \| common_voice_19_0 \| +=========================================+=======+===========+============================+=====================+ \| openai-whisper-large-v3 \| 3.28 \| 7.86 \| 2.85 \| 3.46 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-large-v3-turbo \| 3.64 \| 8.20 \| 3.19 \| 3.85 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-medium \| 5.49 \| 11.13 \| 5.04 \| 5.53 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| primeline-whisper-tiny-german-1224 \| 6.26 \| 9.62 \| 4.97 \| 8.46 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-small \| 9.54 \| 15.94 \| 8.77 \| 10.15 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-base \| 18.75 \| 33.58 \| 17.15 \| 19.74 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ \| openai-whisper-tiny \| 28.80 \| 47.33 \| 26.47 \| 30.76 \| +-----------------------------------------+-------+-----------+----------------------------+---------------------+ ``` \| Size \| Parameters \| \|----------\|------------\| \| tiny \| 39 M \| \| base \| 74 M \| \| small \| 244 M \| \| medium \| 769 M \| \| large \| 1550 M \| \| large-v2 \| 1550 M \| The results are calculated in December 2024 and may change over the time with updates to the eval corpus. For always the newest results please check the code and dataset page. The data and code for evaluations are available [here](https://huggingface.co/datasets/flozi00/asr-german-mixed-evals) ### Training data The training data for this model includes a large amount of spoken German from various sources. The data was carefully selected and processed to optimize recognition performance. The dataset size is about 6.000 hours of public, proprietary and synthetic data. ### Training process The training of the model was performed with the following hyperparameters - Batch size: 32768 - Epochs: 48 - Learning rate: 1e-4 - Data augmentation: No - Optimizer: [Ademamix](https://arxiv.org/abs/2409.03137) ### How to use ```python import torch from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline from datasets import load_dataset device = "cuda:0" if torch.cuda.is_available() else "cpu" torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32 model_id = "primeline/whisper-tiny-german-1224" model = AutoModelForSpeechSeq2Seq.from_pretrained( model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True ) model.to(device) processor = AutoProcessor.from_pretrained(model_id) pipe = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, max_new_tokens=128, chunk_length_s=30, batch_size=16, return_timestamps=True, torch_dtype=torch_dtype, device=device, ) dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation") sample = dataset[0]["audio"] result = pipe(sample) print(result["text"]) ``` ## [About us](https://primeline-ai.com/en/) [![primeline AI](https://primeline-ai.com/wp-content/uploads/2024/02/pl_ai_bildwortmarke_original.svg)](https://primeline-ai.com/en/) Your partner for AI infrastructure in Germany Experience the powerful AI infrastructure that drives your ambitions in Deep Learning, Machine Learning & High-Performance Computing. Optimized for AI training and inference. Model author: [Florian Zimmermeister](https://huggingface.co/flozi00) Disclaimer ``` This model is not a product of the primeLine Group. It represents research conducted by [Florian Zimmermeister](https://huggingface.co/flozi00), with computing power sponsored by primeLine. The model is published under this account by primeLine, but it is not a commercial product of primeLine Solutions GmbH. Please be aware that while we have tested and developed this model to the best of our abilities, errors may still occur. Use of this model is at your own risk. We do not accept liability for any incorrect outputs generated by this model. ```
TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F	TanAlexanderlz	2025-05-26T09:23:04Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-26T08:33:29Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: ALL_RGBCROP_ori16F-8B16F results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ALL_RGBCROP_ori16F-8B16F This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6047 - Accuracy: 0.8443 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 768 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4388 \| 0.125 \| 96 \| 0.4338 \| 0.7988 \| \| 0.2352 \| 1.125 \| 192 \| 0.6832 \| 0.7622 \| \| 0.1411 \| 2.125 \| 288 \| 0.8688 \| 0.8476 \| \| 0.0005 \| 3.125 \| 384 \| 0.9177 \| 0.8354 \| \| 0.0002 \| 4.125 \| 480 \| 1.0111 \| 0.8354 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
Darkknight535/Contrl-Stheno-v1-8B	Darkknight535	2025-05-26T09:22:58Z	7	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Sao10K/L3-8B-Stheno-v3.2", "Delta-Vector/Control-Nanuq-8B", "conversational", "en", "base_model:Delta-Vector/Control-Nanuq-8B", "base_model:merge:Delta-Vector/Control-Nanuq-8B", "base_model:Sao10K/L3-8B-Stheno-v3.2", "base_model:merge:Sao10K/L3-8B-Stheno-v3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-25T08:26:12Z	--- base_model: - Sao10K/L3-8B-Stheno-v3.2 - Delta-Vector/Control-Nanuq-8B tags: - merge - mergekit - lazymergekit - Sao10K/L3-8B-Stheno-v3.2 - Delta-Vector/Control-Nanuq-8B language: - en library_name: transformers --- <style> ebody { font-family: 'Quicksand', sans-serif; background: linear-gradient(135deg, #FF69B4 0%, #800080 100%); color: #FFFFFF; margin: 0; padding: 0; font-size: 16px; min-height: 100vh; } .container { margin: 20px; 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border: 1px solid rgba(255, 20, 147, 0.3); border-radius: 8px; padding: 15px; margin-bottom: 15px; } .review-card:last-child { margin-bottom: 0; } </style> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Contrl-Stheno-8B-v1</title> <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> <link href="styles.css" rel="stylesheet"> </head> <body> <div class="container"> <div class="header"> <h1>Contrl-Stheno-8B-v1</h1> </div> <div class="info"> <img src="https://huggingface.co/Darkknight535/Contrl-Stheno-v1-8B/resolve/main/img_.jpg" alt="Model banner"> <div class="creator-section"> <div class="creator-badge"> <span class="creator-label">Created by</span> <a href="https://huggingface.co/Darkknight535" target="_blank" class="creator-link"> <span class="creator-name">Darkknight535</span> <span class="creator-arrow">→</span> </a> </div> </div> <div class="model-info"> <h2>Model Information</h2> <div class="info-card"> <div class="info-header"> <h3>Contrl-Stheno-8B-v1</h3> <div class="model-tags"> <span class="model-tag">Stheno = Stheno-v3.2</span> <span class="model-tag">Contrl = Control-Nanuq</span> <span class="model-tag">8b Parameters</span> </div> </div> <div class="model-composition"> <h4>Model Composition</h4> <ul class="composition-list"> <li><span class="model-component"><a href="https://huggingface.co/Delta-Vector/Control-Nanuq-8B" target="_blank">Control Nanuq 8B</a></span> Sweetness and Creativity capabilities</li> <li><span class="model-component"><a href="https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2" target="_blank">Stheno-3.2 8B</a></span> Roleplay and logic</li> </ul> </div> <div class="model-description"> <p>An Experiment of mine which turned out to be great! It has dialogues I hadn't found even in 70B models.</p> </div> </div> <!--<div class="metrics-section"> <details open> <summary>User Reviews</summary> <div class="progress-metrics"> <div> <div class="review-card"> <div> <span>[USERNAME]</span> </div> <p>[REVIEW]</p> </div> <div class="review-card"> <div> <span>[USERNAME]</span> </div> <p>[REVIEW]</p> </div> <div class="review-card"> <div> <span>[USERNAME]</span> </div> <p>[REVIEW]</p> </div> </div> </div> </details> </div>--> </div> <div class="section-container"> <h2>Reccomended Templates & Prompts</h2> <div class="template-card"> <div class="template-item"> <div class="template-content"> <a href="" target="_blank" class="template-link"> Sao10k's Euryale System Prompt OR EVA System Prompt <span class="link-arrow">→</span> </a> <span class="template-author">by Sao10k and EVA-UNIT-01</span> </div> </div> </div> </div> <div class="section-container"> <h2>Quantized Versions</h2> <div class="quantized-container"> <div class="quantized-section"> <h3>GGUF Quantizations</h3> <div class="quantized-items"> <div class="quantized-item"> <span class="author">mradermacher</span> <a href="https://huggingface.co/mradermacher/Contrl-Stheno-v1-8B-GGUF" target="_blank"> STATIC-GGUF <span class="link-arrow">→</span> </a> </div> </div> </div> <div class="quantized-section"> <h3>Imat GGUF Quantizations</h3> <div class="quantized-items"> <div class="quantized-item"> <span class="author">mradermacher</span> <a href="https://huggingface.co/mradermacher/Contrl-Stheno-v1-8B-i1-GGUF" target="_blank"> IMAT-GGUF <span class="link-arrow">→</span> </a> </div> </div> </div> </div> </div> <div class="support-section"> <h2>Thanks to these people (I just made a script and Stole SteelSkull's Readme Template)</h2> <div class="support-buttons"> <a href="https://huggingface.co/Sao10k" target="_blank" class="button"> Support Sao10K </a> <a href="https://huggingface.co/Delta-Vector" target="_blank" class="button"> Support Delta-Vector </a> <a href="https://huggingface.co/Steelskull" target="_blank" class="button"> Support SteelSkull </a> </div> </div> </div> </div> </body> </html>
banhkeomath2/sound	banhkeomath2	2025-05-26T09:20:07Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-03-07T05:40:54Z	--- license: apache-2.0 ---
dimasik87/492f5d86-02df-4ab1-809e-25ff65e925e5	dimasik87	2025-05-26T09:19:31Z	0	0	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "llama", "text-generation", "generated_from_trainer", "axolotl", "dpo", "trl", "conversational", "arxiv:2305.18290", "base_model:Orenguteng/Llama-3-8B-Lexi-Uncensored", "base_model:quantized:Orenguteng/Llama-3-8B-Lexi-Uncensored", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-26T08:33:01Z	--- base_model: Orenguteng/Llama-3-8B-Lexi-Uncensored library_name: transformers model_name: 492f5d86-02df-4ab1-809e-25ff65e925e5 tags: - generated_from_trainer - axolotl - dpo - trl licence: license --- # Model Card for 492f5d86-02df-4ab1-809e-25ff65e925e5 This model is a fine-tuned version of [Orenguteng/Llama-3-8B-Lexi-Uncensored](https://huggingface.co/Orenguteng/Llama-3-8B-Lexi-Uncensored). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="dimasik87/492f5d86-02df-4ab1-809e-25ff65e925e5", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/b2djyfkn) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0 - Pytorch: 2.5.0+cu124 - Datasets: 3.0.1 - Tokenizers: 0.20.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
GAYOEN/find-bench	GAYOEN	2025-05-26T09:19:21Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-05-26T09:16:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Yogi99/Aadi9670	Yogi99	2025-05-26T09:19:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-05-26T09:19:09Z	--- license: apache-2.0 ---
mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF	mradermacher	2025-05-26T09:18:44Z	0	1	transformers	[ "transformers", "gguf", "en", "dataset:BeaverAI/REDACTED1", "dataset:BeaverAI/REDACTED2", "dataset:BeaverAI/REDACTED3", "dataset:BeaverAI/REDACTED4", "dataset:BeaverAI/REDACTED5", "dataset:BeaverAI/REDACTED6", "dataset:PJMixers-Dev/Lit-axo-Shuffled", "dataset:PJMixers-Dev/Mielikki_Erebus-87k-axo", "dataset:PJMixers/RyokoAI_Honeyfeed3600-Cleanish", "dataset:PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo", "dataset:Nelathan/synthetic-sugar-quill", "dataset:PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long", "dataset:PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned", "dataset:PJMixers-Dev/Subtitles", "dataset:PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo", "dataset:PJMixers/AP-News-2024", "dataset:PJMixers-Dev/Fundus-AP-News-Formatted", "dataset:PJMixers-Dev/Fundus-AP-News-2-Formatted", "dataset:PJMixers-Dev/goodwiki-2024-12-04-axo", "dataset:epfl-llm/guidelines", "dataset:PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT", "dataset:OpenLeecher/lmsys_chat_1m_clean", "dataset:PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed", "dataset:allura-org/gryphe-sonnet-3.5-charcards-names-added", "dataset:anthracite-org/c2_logs_32k_llama3_qwen2_v1.3", "dataset:PJMixers-Dev/MinervaAI_Aesir-Preview-Anon", "dataset:PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT", "dataset:PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT", "dataset:grimulkan/aicg-logs-augmented", "dataset:grimulkan/PIPPA-augmented-dedup", "dataset:PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted", "dataset:PJMixers/lodrick-the-lafted_OpusStories-ShareGPT", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Gryphe/Opus-WritingPrompts", "dataset:anthracite-org/nopm_claude_writing_fixed", "dataset:PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT", "dataset:allura-org/fujin-instruct-v2", "dataset:ToastyPigeon/gutenberg-sft", "dataset:PocketDoc/Dans-Prosemaxx-Adventure", "dataset:PocketDoc/Dans-Failuremaxx-Adventure-3", "dataset:TheDrummer/AmoralQA-v2", "base_model:PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M", "base_model:quantized:PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2025-05-25T20:42:33Z	--- base_model: PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M datasets: - BeaverAI/REDACTED1 - BeaverAI/REDACTED2 - BeaverAI/REDACTED3 - BeaverAI/REDACTED4 - BeaverAI/REDACTED5 - BeaverAI/REDACTED6 - PJMixers-Dev/Lit-axo-Shuffled - PJMixers-Dev/Mielikki_Erebus-87k-axo - PJMixers/RyokoAI_Honeyfeed3600-Cleanish - PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo - Nelathan/synthetic-sugar-quill - PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long - PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned - PJMixers-Dev/Subtitles - PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo - PJMixers/AP-News-2024 - PJMixers-Dev/Fundus-AP-News-Formatted - PJMixers-Dev/Fundus-AP-News-2-Formatted - PJMixers-Dev/goodwiki-2024-12-04-axo - epfl-llm/guidelines - PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT - OpenLeecher/lmsys_chat_1m_clean - PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed - allura-org/gryphe-sonnet-3.5-charcards-names-added - anthracite-org/c2_logs_32k_llama3_qwen2_v1.3 - PJMixers-Dev/MinervaAI_Aesir-Preview-Anon - PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT - PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT - grimulkan/aicg-logs-augmented - grimulkan/PIPPA-augmented-dedup - PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted - PJMixers/lodrick-the-lafted_OpusStories-ShareGPT - Gryphe/ChatGPT-4o-Writing-Prompts - Gryphe/Opus-WritingPrompts - anthracite-org/nopm_claude_writing_fixed - PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT - allura-org/fujin-instruct-v2 - ToastyPigeon/gutenberg-sft - PocketDoc/Dans-Prosemaxx-Adventure - PocketDoc/Dans-Failuremaxx-Adventure-3 - TheDrummer/AmoralQA-v2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q2_K.gguf) \| i1-Q2_K \| 1.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 1.7 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 1.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-IQ4_NL.gguf) \| i1-IQ4_NL \| 2.0 \| prefer IQ4_XS \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.0 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q4_1.gguf) \| i1-Q4_1 \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Granite-3.1-Earthen-v0.3-3B-A800M-i1-GGUF/resolve/main/Granite-3.1-Earthen-v0.3-3B-A800M.i1-Q6_K.gguf) \| i1-Q6_K \| 2.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
phospho-app/jmota27-gr00t-boat_cup_dataset-npbv8	phospho-app	2025-05-26T09:15:29Z	0	0	null	[ "safetensors", "gr00t_n1", "phosphobot", "gr00t", "region:us" ]	null	2025-05-26T08:49:06Z	--- tags: - phosphobot - gr00t task_categories: - robotics --- # gr00t Model - phospho Training Pipeline ## This model was trained using phospho. Training was successfull, try it out on your robot! ## Training parameters: - Dataset: [jmota27/boat_cup_dataset](https://huggingface.co/datasets/jmota27/boat_cup_dataset) - Wandb run URL: None - Epochs: 10 - Batch size: 49 - Training steps: None 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
wuxia196/dqn-SpaceInvadersNoFrameskip-v4	wuxia196	2025-05-26T09:15:21Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-26T07:43:10Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 488.00 +/- 141.16 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib SBX (SB3 + Jax): https://github.com/araffin/sbx Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga wuxia196 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga wuxia196 -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga wuxia196 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 128), ('buffer_size', 200000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0002), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
Hyaline/Domaino1s-finance	Hyaline	2025-05-26T09:13:41Z	1	0	null	[ "safetensors", "qwen2", "arxiv:2501.14431", "license:apache-2.0", "region:us" ]	null	2025-01-19T01:32:02Z	--- license: apache-2.0 --- This repository stores the model parameters for our paper [Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains](https://arxiv.org/abs/2501.14431). Our paper is now accepted as findings of ACL 2025. More details: [Domaino1s](https://github.com/Hyalinesky/Domaino1s) ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{chu2025domaino1s, title={Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains}, author={Chu, Xu and Tan, Zhijie and Xue, Hanlin and Wang, Guanyu and Mo, Tong and Li, Weiping}, journal={arXiv preprint arXiv:2501.14431}, year={2025} } ```
ashani/ppo-SnowballTarget	ashani	2025-05-26T09:09:56Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	reinforcement-learning	2025-05-26T09:09:52Z	--- library_name: ml-agents tags: - SnowballTarget - deep-reinforcement-learning - reinforcement-learning - ML-Agents-SnowballTarget --- # ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: ashani/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
ksun1ch/Salesforce-codet5-base-nl2vis-for-nl2sql	ksun1ch	2025-05-26T09:08:10Z	0	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-26T08:49:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ahmedelgebaly/llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E2	ahmedelgebaly	2025-05-26T09:07:12Z	16	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-13T14:02:31Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal type: alpaca split: train test_datasets: - path: ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 #Before it was 16 lora_dropout: 0.05 lora_target_modules: #Before it was empty - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E2 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E2 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E2 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 2 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: true #Before it was false bf16: auto tf32: false gradient_checkpointing: true flash_attention: true warmup_steps: 50 #Before it was 10 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.0 special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8821 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0036 \| 1 \| 1.7171 \| \| 0.871 \| 0.2527 \| 70 \| 0.9221 \| \| 0.8191 \| 0.5054 \| 140 \| 0.8980 \| \| 0.8102 \| 0.7581 \| 210 \| 0.8845 \| \| 0.6998 \| 1.0081 \| 280 \| 0.8708 \| \| 0.6609 \| 1.2608 \| 350 \| 0.8868 \| \| 0.661 \| 1.5135 \| 420 \| 0.8825 \| \| 0.6388 \| 1.7662 \| 490 \| 0.8821 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
ahmedelgebaly/llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E3	ahmedelgebaly	2025-05-26T09:06:51Z	24	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-13T14:02:32Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal type: alpaca split: train test_datasets: - path: ahmedelgebaly/SQuad_SciQ_HotpotQA_Alpaca_Equal type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 #Before it was 16 lora_dropout: 0.05 lora_target_modules: #Before it was empty - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E3 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E3 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E3 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 3 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: true #Before it was false bf16: auto tf32: false gradient_checkpointing: true flash_attention: true warmup_steps: 50 #Before it was 10 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.0 special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-Squad_SciQ_HotpotQA_Equal_E3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9982 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0036 \| 1 \| 1.7171 \| \| 0.8618 \| 0.2527 \| 70 \| 0.9238 \| \| 0.801 \| 0.5054 \| 140 \| 0.8990 \| \| 0.8208 \| 0.7581 \| 210 \| 0.8868 \| \| 0.7207 \| 1.0081 \| 280 \| 0.8757 \| \| 0.6826 \| 1.2608 \| 350 \| 0.8944 \| \| 0.6631 \| 1.5135 \| 420 \| 0.8995 \| \| 0.6935 \| 1.7662 \| 490 \| 0.8873 \| \| 0.4885 \| 2.0171 \| 560 \| 0.9172 \| \| 0.4611 \| 2.2699 \| 630 \| 0.9876 \| \| 0.4425 \| 2.5226 \| 700 \| 0.9970 \| \| 0.4339 \| 2.7753 \| 770 \| 0.9982 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
sp-embraceable/Phi4-FT-unsloth-runpod-2500steps-e1-above90-adapter	sp-embraceable	2025-05-26T09:05:11Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/phi-4", "base_model:adapter:unsloth/phi-4", "region:us" ]	null	2025-05-26T09:01:31Z	--- base_model: unsloth/Phi-4 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
yukieos/gemma-2-2b-it-lora	yukieos	2025-05-26T09:04:56Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:google/gemma-2-2b-it", "base_model:adapter:google/gemma-2-2b-it", "license:gemma", "region:us" ]	null	2025-05-26T07:56:01Z	--- library_name: peft license: gemma base_model: google/gemma-2-2b-it tags: - generated_from_trainer model-index: - name: gemma-2-2b-it-lora results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-2-2b-it-lora This model is a fine-tuned version of [google/gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e4	ahmedelgebaly	2025-05-26T09:04:54Z	13	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-23T13:17:58Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_e4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used # Load your previously fine-tuned model as a PEFT adapter peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_e4 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e4 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2_SciQ_e4 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e4 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 4 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_e4 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7866 \| 0.0305 \| 1 \| 1.8420 \| \| 1.1285 \| 0.2443 \| 8 \| 1.0966 \| \| 0.841 \| 0.4885 \| 16 \| 0.9650 \| \| 0.8667 \| 0.7328 \| 24 \| 0.9336 \| \| 0.8586 \| 0.9771 \| 32 \| 0.9192 \| \| 0.8353 \| 1.2137 \| 40 \| 0.9084 \| \| 0.8003 \| 1.4580 \| 48 \| 0.9014 \| \| 0.8299 \| 1.7023 \| 56 \| 0.8971 \| \| 0.8033 \| 1.9466 \| 64 \| 0.8940 \| \| 0.81 \| 2.1832 \| 72 \| 0.8922 \| \| 0.81 \| 2.4275 \| 80 \| 0.8918 \| \| 0.7858 \| 2.6718 \| 88 \| 0.8887 \| \| 0.7595 \| 2.9160 \| 96 \| 0.8882 \| \| 0.7574 \| 3.1527 \| 104 \| 0.8884 \| \| 0.7754 \| 3.3969 \| 112 \| 0.8896 \| \| 0.7671 \| 3.6412 \| 120 \| 0.8894 \| \| 0.7495 \| 3.8855 \| 128 \| 0.8895 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
Datasmartly/nllb-tamazight-finetunedmixe1	Datasmartly	2025-05-26T09:04:30Z	0	0	null	[ "safetensors", "m2m_100", "generated_from_trainer", "base_model:facebook/nllb-200-3.3B", "base_model:finetune:facebook/nllb-200-3.3B", "license:cc-by-nc-4.0", "region:us" ]	null	2025-05-26T08:49:01Z	--- license: cc-by-nc-4.0 base_model: facebook/nllb-200-3.3B tags: - generated_from_trainer model-index: - name: nllb-tamazight-finetunedmixe1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nllb-tamazight-finetunedmixe1 This model is a fine-tuned version of [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2100 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0406 \| 1.0 \| 225 \| 0.7700 \| \| 0.1517 \| 2.0 \| 450 \| 0.1944 \| \| 0.0553 \| 3.0 \| 675 \| 0.2100 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.4.1+cu124 - Datasets 3.6.0 - Tokenizers 0.15.2
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_E5_V2	ahmedelgebaly	2025-05-26T09:04:29Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-22T22:43:46Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_E5_V2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3-8B lora_model_dir: ahmedelgebaly/llama-3.1-8b-squadv2_E5_V2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train - path: ahmedelgebaly/SQuad_2_Alpaca type: alpaca split: train percentage: 0.1 # small replay buffer to avoid forgetting test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 #Before it was 16 lora_dropout: 0.05 lora_target_modules: #Before it was empty - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e5_v2 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2-v0_SciQ_e5_v2 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_E5_V2 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 5 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: true #Before it was false bf16: auto tf32: false gradient_checkpointing: true flash_attention: true warmup_steps: 50 #Before it was 10 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.0 special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_E5_V2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3837 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0029 \| 1 \| 3.2812 \| \| 0.5424 \| 0.2504 \| 85 \| 0.9365 \| \| 0.5614 \| 0.5007 \| 170 \| 0.9119 \| \| 0.5595 \| 0.7511 \| 255 \| 0.9003 \| \| 0.5602 \| 1.0015 \| 340 \| 0.8944 \| \| 0.3603 \| 1.2496 \| 425 \| 0.9270 \| \| 0.3926 \| 1.5 \| 510 \| 0.9218 \| \| 0.4007 \| 1.7504 \| 595 \| 0.9171 \| \| 0.3932 \| 2.0007 \| 680 \| 0.9158 \| \| 0.2447 \| 2.2489 \| 765 \| 1.0126 \| \| 0.2629 \| 2.4993 \| 850 \| 0.9968 \| \| 0.2566 \| 2.7496 \| 935 \| 1.0126 \| \| 0.2551 \| 3.0 \| 1020 \| 1.0091 \| \| 0.1665 \| 3.2482 \| 1105 \| 1.1822 \| \| 0.1683 \| 3.4985 \| 1190 \| 1.1927 \| \| 0.1717 \| 3.7489 \| 1275 \| 1.1943 \| \| 0.1718 \| 3.9993 \| 1360 \| 1.1832 \| \| 0.1133 \| 4.2474 \| 1445 \| 1.3833 \| \| 0.1158 \| 4.4978 \| 1530 \| 1.3814 \| \| 0.1175 \| 4.7482 \| 1615 \| 1.3837 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
ai-forever/FRIDA	ai-forever	2025-05-26T09:04:19Z	13,940	54	sentence-transformers	[ "sentence-transformers", "safetensors", "t5", "mteb", "transformers", "feature-extraction", "ru", "en", "dataset:ai-forever/solyanka", "arxiv:2309.10931", "arxiv:2408.12503", "base_model:ai-forever/FRED-T5-1.7B", "base_model:finetune:ai-forever/FRED-T5-1.7B", "license:mit", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-12-26T15:07:35Z	--- model-index: - name: FRIDA results: - dataset: config: default name: MTEB CEDRClassification (default) revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4 split: test type: ai-forever/cedr-classification metrics: - type: accuracy value: 64.60148777895856 - type: f1 value: 70.36630348039266 - type: lrap value: 92.47290116896953 - type: main_score value: 64.60148777895856 task: type: MultilabelClassification - dataset: config: default name: MTEB GeoreviewClassification (default) revision: 3765c0d1de6b7d264bc459433c45e5a75513839c split: test type: ai-forever/georeview-classification metrics: - type: accuracy value: 57.70996093750001 - type: f1 value: 53.18542982057098 - type: f1_weighted value: 53.17663229582108 - type: main_score value: 57.70996093750001 task: type: Classification - dataset: config: default name: MTEB GeoreviewClusteringP2P (default) revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec split: test type: ai-forever/georeview-clustering-p2p metrics: - type: main_score value: 78.25468393043356 - type: v_measure value: 78.25468393043356 - type: v_measure_std value: 0.5094366871364238 task: type: Clustering - dataset: config: default name: MTEB HeadlineClassification (default) revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb split: test type: ai-forever/headline-classification metrics: - type: accuracy value: 89.0185546875 - type: f1 value: 88.993933120612 - type: f1_weighted value: 88.99276764225768 - type: main_score value: 89.0185546875 task: type: Classification - dataset: config: default name: MTEB InappropriatenessClassification (default) revision: 601651fdc45ef243751676e62dd7a19f491c0285 split: test type: ai-forever/inappropriateness-classification metrics: - type: accuracy value: 78.330078125 - type: ap value: 73.17856750532495 - type: ap_weighted value: 73.17856750532495 - type: f1 value: 78.20169867599041 - type: f1_weighted value: 78.20169867599041 - type: main_score value: 78.330078125 task: type: Classification - dataset: config: default name: MTEB KinopoiskClassification (default) revision: 5911f26666ac11af46cb9c6849d0dc80a378af24 split: test type: ai-forever/kinopoisk-sentiment-classification metrics: - type: accuracy value: 70.46666666666665 - type: f1 value: 65.83951766538878 - type: f1_weighted value: 65.83951766538878 - type: main_score value: 70.46666666666665 task: type: Classification - dataset: config: ru name: MTEB MIRACLReranking (ru) revision: 6d1962c527217f8927fca80f890f14f36b2802af split: dev type: miracl/mmteb-miracl-reranking metrics: - type: MAP@1(MIRACL) value: 39.023 - type: MAP@10(MIRACL) value: 60.208 - type: MAP@100(MIRACL) value: 61.672000000000004 - type: MAP@1000(MIRACL) value: 61.672000000000004 - type: MAP@20(MIRACL) value: 61.30799999999999 - type: MAP@3(MIRACL) value: 53.33 - type: MAP@5(MIRACL) value: 57.289 - type: NDCG@1(MIRACL) value: 63.352 - type: NDCG@10(MIRACL) value: 66.042 - type: NDCG@100(MIRACL) value: 68.702 - type: NDCG@1000(MIRACL) value: 68.702 - type: NDCG@20(MIRACL) value: 67.768 - type: NDCG@3(MIRACL) value: 61.925 - type: NDCG@5(MIRACL) value: 63.327 - type: P@1(MIRACL) value: 63.352 - type: P@10(MIRACL) value: 16.512 - type: P@100(MIRACL) value: 1.9529999999999998 - type: P@1000(MIRACL) value: 0.19499999999999998 - type: P@20(MIRACL) value: 9.13 - type: P@3(MIRACL) value: 37.878 - type: P@5(MIRACL) value: 27.586 - type: Recall@1(MIRACL) value: 39.023 - type: Recall@10(MIRACL) value: 72.35000000000001 - type: Recall@100(MIRACL) value: 79.952 - type: Recall@1000(MIRACL) value: 79.952 - type: Recall@20(MIRACL) value: 76.828 - type: Recall@3(MIRACL) value: 57.769999999999996 - type: Recall@5(MIRACL) value: 64.91900000000001 - type: main_score value: 66.042 - type: nAUC_MAP@1000_diff1(MIRACL) value: 27.150388833033052 - type: nAUC_MAP@1000_max(MIRACL) value: 55.15672274267081 - type: nAUC_MAP@1000_std(MIRACL) value: 30.088939934575553 - type: nAUC_MAP@100_diff1(MIRACL) value: 27.150388833033052 - type: nAUC_MAP@100_max(MIRACL) value: 55.15672274267081 - type: nAUC_MAP@100_std(MIRACL) value: 30.088939934575553 - type: nAUC_MAP@10_diff1(MIRACL) value: 27.853691773641742 - type: nAUC_MAP@10_max(MIRACL) value: 52.89390350055654 - type: nAUC_MAP@10_std(MIRACL) value: 28.08732516551691 - type: nAUC_MAP@1_diff1(MIRACL) value: 43.23179150244192 - type: nAUC_MAP@1_max(MIRACL) value: 29.923943954188864 - type: nAUC_MAP@1_std(MIRACL) value: 7.447084370195121 - type: nAUC_MAP@20_diff1(MIRACL) value: 27.328384072311675 - type: nAUC_MAP@20_max(MIRACL) value: 54.60286379835721 - type: nAUC_MAP@20_std(MIRACL) value: 29.8084128980043 - type: nAUC_MAP@3_diff1(MIRACL) value: 31.244971536944554 - type: nAUC_MAP@3_max(MIRACL) value: 43.63984692803854 - type: nAUC_MAP@3_std(MIRACL) value: 18.609234683765887 - type: nAUC_MAP@5_diff1(MIRACL) value: 29.088760492638286 - type: nAUC_MAP@5_max(MIRACL) value: 48.30474364461509 - type: nAUC_MAP@5_std(MIRACL) value: 23.817514353844224 - type: nAUC_NDCG@1000_diff1(MIRACL) value: 23.12754356408408 - type: nAUC_NDCG@1000_max(MIRACL) value: 64.24894553363303 - type: nAUC_NDCG@1000_std(MIRACL) value: 38.19318050598967 - type: nAUC_NDCG@100_diff1(MIRACL) value: 23.12754356408408 - type: nAUC_NDCG@100_max(MIRACL) value: 64.24894553363303 - type: nAUC_NDCG@100_std(MIRACL) value: 38.19318050598967 - type: nAUC_NDCG@10_diff1(MIRACL) value: 24.779856373697275 - type: nAUC_NDCG@10_max(MIRACL) value: 60.4054459738118 - type: nAUC_NDCG@10_std(MIRACL) value: 35.148950441182784 - type: nAUC_NDCG@1_diff1(MIRACL) value: 35.605865569438556 - type: nAUC_NDCG@1_max(MIRACL) value: 65.77787399715454 - type: nAUC_NDCG@1_std(MIRACL) value: 34.34726892885082 - type: nAUC_NDCG@20_diff1(MIRACL) value: 23.71231783125691 - type: nAUC_NDCG@20_max(MIRACL) value: 62.89676599488004 - type: nAUC_NDCG@20_std(MIRACL) value: 37.697052941884316 - type: nAUC_NDCG@3_diff1(MIRACL) value: 26.109027741640865 - type: nAUC_NDCG@3_max(MIRACL) value: 56.22356793638693 - type: nAUC_NDCG@3_std(MIRACL) value: 29.9437568508688 - type: nAUC_NDCG@5_diff1(MIRACL) value: 25.98644715327336 - type: nAUC_NDCG@5_max(MIRACL) value: 56.25032008404774 - type: nAUC_NDCG@5_std(MIRACL) value: 31.581899860862578 - type: nAUC_P@1000_diff1(MIRACL) value: -18.29912787064644 - type: nAUC_P@1000_max(MIRACL) value: 31.811344878776087 - type: nAUC_P@1000_std(MIRACL) value: 30.163820183304914 - type: nAUC_P@100_diff1(MIRACL) value: -18.299127870646405 - type: nAUC_P@100_max(MIRACL) value: 31.811344878776133 - type: nAUC_P@100_std(MIRACL) value: 30.163820183304956 - type: nAUC_P@10_diff1(MIRACL) value: -15.96416268531149 - type: nAUC_P@10_max(MIRACL) value: 36.989578896466526 - type: nAUC_P@10_std(MIRACL) value: 34.54507111688143 - type: nAUC_P@1_diff1(MIRACL) value: 35.605865569438556 - type: nAUC_P@1_max(MIRACL) value: 65.77787399715454 - type: nAUC_P@1_std(MIRACL) value: 34.34726892885082 - 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The model is based on the encoder part of [FRED-T5](https://arxiv.org/abs/2309.10931) model and continues research of text embedding models ([ruMTEB](https://arxiv.org/abs/2408.12503), [ru-en-RoSBERTa](https://huggingface.co/ai-forever/ru-en-RoSBERTa)). It has been pre-trained on a Russian-English dataset and fine-tuned for improved performance on the target task. For more model details please refer to our [article](https://habr.com/ru/companies/sberdevices/articles/909924/) (RU). ## Usage The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task. We use the following basic rules to choose a prefix: - `"search_query: "` and `"search_document: "` prefixes are for answer or relevant paragraph retrieval - `"paraphrase: "` prefix is for symmetric paraphrasing related tasks (STS, paraphrase mining, deduplication) - `"categorize: "` prefix is for asymmetric matching of document title and body (e.g. news, scientific papers, social posts) - `"categorize_sentiment: "` prefix is for any tasks that rely on sentiment features (e.g. hate, toxic, emotion) - `"categorize_topic: "` prefix is intended for tasks where you need to group texts by topic - `"categorize_entailment: "` prefix is for textual entailment task (NLI) To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets. Below are examples of texts encoding using the Transformers and SentenceTransformers libraries. ### Transformers ```python import torch import torch.nn.functional as F from transformers import AutoTokenizer, T5EncoderModel def pool(hidden_state, mask, pooling_method="cls"): if pooling_method == "mean": s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1) d = mask.sum(axis=1, keepdim=True).float() return s / d elif pooling_method == "cls": return hidden_state[:, 0] inputs = [ # "paraphrase: В Ярославской области разрешили работу бань, но без посетителей", "categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.", "search_query: Сколько программистов нужно, чтобы вкрутить лампочку?", # "paraphrase: Ярославским баням разрешили работать без посетителей", "categorize_entailment: Женщину спасают врачи.", "search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование." ] tokenizer = AutoTokenizer.from_pretrained("ai-forever/FRIDA") model = T5EncoderModel.from_pretrained("ai-forever/FRIDA") tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): outputs = model(**tokenized_inputs) embeddings = pool( outputs.last_hidden_state, tokenized_inputs["attention_mask"], pooling_method="cls" # or try "mean" ) embeddings = F.normalize(embeddings, p=2, dim=1) sim_scores = embeddings[:3] @ embeddings[3:].T print(sim_scores.diag().tolist()) # [0.9360030293464661, 0.8591322302818298, 0.728583037853241] ``` ### SentenceTransformers ```python from sentence_transformers import SentenceTransformer inputs = [ # "paraphrase: В Ярославской области разрешили работу бань, но без посетителей", "categorize_entailment: Женщину доставили в больницу, за ее жизнь сейчас борются врачи.", "search_query: Сколько программистов нужно, чтобы вкрутить лампочку?", # "paraphrase: Ярославским баням разрешили работать без посетителей", "categorize_entailment: Женщину спасают врачи.", "search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование." ] # loads model with CLS pooling model = SentenceTransformer("ai-forever/FRIDA") # embeddings are normalized by default embeddings = model.encode(inputs, convert_to_tensor=True) sim_scores = embeddings[:3] @ embeddings[3:].T print(sim_scores.diag().tolist()) # [0.9360026717185974, 0.8591331243515015, 0.7285830974578857] ``` or using prompts (sentence-transformers>=2.4.0): ```python from sentence_transformers import SentenceTransformer # loads model with CLS pooling model = SentenceTransformer("ai-forever/FRIDA") paraphrase = model.encode(["В Ярославской области разрешили работу бань, но без посетителей", "Ярославским баням разрешили работать без посетителей"], prompt_name="paraphrase") print(paraphrase[0] @ paraphrase[1].T) # 0.9360032 categorize_entailment = model.encode(["Женщину доставили в больницу, за ее жизнь сейчас борются врачи.", "Женщину спасают врачи."], prompt_name="categorize_entailment") print(categorize_entailment[0] @ categorize_entailment[1].T) # 0.8591322 query_embedding = model.encode("Сколько программистов нужно, чтобы вкрутить лампочку?", prompt_name="search_query") document_embedding = model.encode("Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", prompt_name="search_document") print(query_embedding @ document_embedding.T) # 0.7285831 ``` ## Authors + [SaluteDevices](https://sberdevices.ru/) AI for B2C RnD Team. + Artem Snegirev: [HF profile](https://huggingface.co/artemsnegirev), [Github](https://github.com/artemsnegirev); + Anna Maksimova [HF profile](https://huggingface.co/anpalmak); + Aleksandr Abramov: [HF profile](https://huggingface.co/Andrilko), [Github](https://github.com/Ab1992ao), [Kaggle Competitions Master](https://www.kaggle.com/andrilko) ## Citation ``` @misc{TODO } ``` ## Limitations The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.
Hyper-AI-Computer/FlaxLlama-Init-Model-V4	Hyper-AI-Computer	2025-05-26T09:04:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T08:53:52Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description ```json { "bytes accessed0{}":38364528640.0, "transcendentals":2114048000.0, "utilization1{}":583.0, "bytes accessed1{}":32694747136.0, "utilization2{}":61.0, "bytes accessedout{}":38372065280.0, "utilization0{}":554.0, "bytes accessed2{}":2025914368.0, "bytes accessed":95467569152.0, "flops":3053315162112.0 } ``` This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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foreseeitwithme/real-estate-qa-synthetic	foreseeitwithme	2025-05-26T09:03:24Z	0	0	transformers	[ "transformers", "safetensors", "dataset:foreseeitwithme/real-estate-qa-synthetic", "endpoints_compatible", "region:us" ]	null	2025-05-26T08:18:30Z	--- library_name: transformers datasets: - foreseeitwithme/real-estate-qa-synthetic metrics: - bertscore --- # Model Card for foreseeitwithme/real-estate-qa-synthetic ## Model Details ### Model Description This model is a fine-tuned language model based on Qwen1.5-0.5B, specialized for answering real estate related questions. It was trained on a synthetic dataset designed to cover common queries about property buying, selling, legal considerations, and terminology differences in the Indian real estate context. - Developed by: [Priyanka S] - Model type: Transformer-based causal language model (Qwen1.5-0.5B) - Language(s) (NLP): English - License: [] - Finetuned from model: Qwen1.5-0.5B ### Model Sources - Repository: https://huggingface.co/foreseeitwithme/real-estate-qa-synthetic - Dataset: foreseeitwithme/real-estate-qa-synthetic - Paper/Demo: Not available ## Uses ### Direct Use This model can be used to provide answers to frequently asked questions related to Indian real estate, such as document requirements, price ranges, legal issues, and terminology explanations. ### Downstream Use It can be integrated into chatbots, customer support systems, or property listing platforms to improve user experience by providing accurate, domain-specific responses. ### Out-of-Scope Use Not suitable for legal advice or complex property transaction decisions. It should not replace consultation with qualified legal professionals. ## Bias, Risks, and Limitations The model is trained on synthetic data and may not cover all real-world edge cases or regional variations comprehensively. There is a risk of outdated or inaccurate legal information. ### Recommendations Users should verify critical information with official sources. Continuous updates and retraining are recommended to maintain accuracy. ## How to Get Started with the Model Use the Hugging Face Transformers pipeline with your preferred framework: ```python from transformers import pipeline qa_pipeline = pipeline("question-answering", model="foreseeitwithme/real-estate-qa-synthetic") result = qa_pipeline({ "question": "What documents are required for buying a flat in Bangalore?", "context": "Provide any relevant text or context here if needed." }) print(result)
ProductGuySensei/imagesofbro	ProductGuySensei	2025-05-26T09:03:21Z	13	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-20T15:10:12Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: cover --- # Imagesofbro <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `cover` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "cover", "lora_weights": "https://huggingface.co/ProductGuySensei/imagesofbro/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('ProductGuySensei/imagesofbro', weight_name='lora.safetensors') image = pipeline('cover').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/ProductGuySensei/imagesofbro/discussions) to add images that show off what you’ve made with this LoRA.
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_HotpotQa_e1	ahmedelgebaly	2025-05-26T09:02:42Z	8	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T19:47:49Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_HotpotQa_e1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used # Load your previously fine-tuned model as a PEFT adapter peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e1 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/HotpotQA_Alpaca type: alpaca split: train test_datasets: - path: ahmedelgebaly/HotpotQA_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_HotpotQa_e1 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2_SciQ_HotpotQa_e1 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_HotpotQa_e1 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 1 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_HotpotQa_e1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7263 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5019 \| 0.0005 \| 1 \| 1.6930 \| \| 0.6246 \| 0.2501 \| 486 \| 0.7879 \| \| 0.6935 \| 0.5001 \| 972 \| 0.7512 \| \| 0.5706 \| 0.7502 \| 1458 \| 0.7263 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
rafaelrnzo/mistral-summarize-16bit-7b	rafaelrnzo	2025-05-26T09:02:24Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "unsloth", "trl", "sft", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T08:38:23Z	--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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bigband/EnchantingDumuzi	bigband	2025-05-26T09:02:21Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T08:53:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
cheetahbooked/rl-doom-health-gathering-supreme	cheetahbooked	2025-05-26T09:01:50Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2025-05-25T04:48:53Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: doom_health_gathering_supreme type: doom_health_gathering_supreme metrics: - type: mean_reward value: 6.50 +/- 2.70 name: mean_reward verified: false --- A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r cheetahbooked/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_HotpotQa_e2	ahmedelgebaly	2025-05-26T09:01:38Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-24T20:16:35Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_HotpotQa_e2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used # Load your previously fine-tuned model as a PEFT adapter peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/HotpotQA_Alpaca type: alpaca split: train test_datasets: - path: ahmedelgebaly/HotpotQA_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_HotpotQa_e2 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2_SciQ_HotpotQa_e2 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_HotpotQa_e2 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 2 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_HotpotQa_e2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7109 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5019 \| 0.0005 \| 1 \| 1.6930 \| \| 0.6274 \| 0.2501 \| 486 \| 0.7914 \| \| 0.6962 \| 0.5001 \| 972 \| 0.7567 \| \| 0.5719 \| 0.7502 \| 1458 \| 0.7311 \| \| 0.6021 \| 1.0003 \| 1944 \| 0.7159 \| \| 0.5002 \| 1.2483 \| 2430 \| 0.7223 \| \| 0.5363 \| 1.4983 \| 2916 \| 0.7147 \| \| 0.5215 \| 1.7484 \| 3402 \| 0.7109 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
Nerva1228/kxnainai	Nerva1228	2025-05-26T09:00:58Z	13	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-23T08:13:09Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: kxnainai --- # Kxnainai <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `kxnainai` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kxnainai", "lora_weights": "https://huggingface.co/Nerva1228/kxnainai/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Nerva1228/kxnainai', weight_name='lora.safetensors') image = pipeline('kxnainai').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/kxnainai/discussions) to add images that show off what you’ve made with this LoRA.
leobianco/npov_RM_model_google_seed_12345_SYN_LLM_false_SYN_STRUCT_false_epochs_3_lr_1e-4_lora_1	leobianco	2025-05-26T09:00:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T08:50:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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VamshiSurya/CareerCompassModel	VamshiSurya	2025-05-26T08:59:49Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-05-26T08:58:37Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: VamshiSurya - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Aluba/zombie2505_28	Aluba	2025-05-26T08:57:38Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-26T08:30:08Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
bigband/IllustriousAmun	bigband	2025-05-26T08:57:21Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T08:46:00Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
Aluba/zombie2505_26	Aluba	2025-05-26T08:56:49Z	0	0	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2025-05-26T08:29:54Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_E2_V2	ahmedelgebaly	2025-05-26T08:54:06Z	12	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-09T15:16:11Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_E2_V2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B lora_model_dir: ahmedelgebaly/llama-3.1-8b-squadv2_E1_V2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train - path: ahmedelgebaly/SQuad_2_Alpaca type: alpaca split: train percentage: 0.1 # small replay buffer to avoid forgetting test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 #Before it was 16 lora_dropout: 0.05 lora_target_modules: #Before it was empty - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e2_v2 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2-v0_SciQ_e2_v2 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_E2_V2 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 2 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: true #Before it was false bf16: auto tf32: false gradient_checkpointing: true flash_attention: true warmup_steps: 50 #Before it was 10 evals_per_epoch: 4 saves_per_epoch: 1 weight_decay: 0.0 special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_E2_V2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8990 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0029 \| 1 \| 2.2993 \| \| 0.8102 \| 0.2504 \| 85 \| 0.9110 \| \| 0.8141 \| 0.5007 \| 170 \| 0.8933 \| \| 0.8189 \| 0.7511 \| 255 \| 0.8846 \| \| 0.8188 \| 1.0015 \| 340 \| 0.8763 \| \| 0.6354 \| 1.2496 \| 425 \| 0.9022 \| \| 0.6568 \| 1.5 \| 510 \| 0.9029 \| \| 0.639 \| 1.7504 \| 595 \| 0.8990 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
pwde/tuiche-ceshi	pwde	2025-05-26T08:52:54Z	0	0	null	[ "license:other", "region:us" ]	null	2025-05-26T08:01:47Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e1_v3	ahmedelgebaly	2025-05-26T08:51:59Z	16	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-27T20:13:43Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_e1_v3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B peft_model: ahmedelgebaly/llama-3.1-8b-squadv2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train - path: ahmedelgebaly/SQuad_2_Alpaca type: alpaca split: train percentage: 0.1 # small replay buffer to avoid forgetting test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out-mixed adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: - q_proj - k_proj - v_proj - o_proj - gate_proj - up_proj - down_proj #lora_target_linear: true ##Removed for Explicit Control after adding modules lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e1_v3 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2-v0_SciQ_e1_v2 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e1_v3 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 1 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0001 ##Reducing from 0.0002 to 0.0001 lr_scheduler_warmup_ratio: 0.1 ##Added for better convergence very low for SciQ (only ~11k samples train_on_inputs: false group_by_length: true bf16: auto fp16: tf32: false gradient_checkpointing: true logging_steps: 1 xformers_attention: flash_attention: true evals_per_epoch: 4 saves_per_epoch: 1 log_eval_metric: true weight_decay: 0.0 special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_e1_v3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8983 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.5496 \| 0.0029 \| 1 \| 1.8420 \| \| 0.9073 \| 0.2504 \| 85 \| 0.9289 \| \| 0.9158 \| 0.5007 \| 170 \| 0.9048 \| \| 0.8754 \| 0.7511 \| 255 \| 0.8983 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
Pretrain-FBK-NLP/mt5-large_AllDataSourcesClinical_0.0002_constant_1024_paper	Pretrain-FBK-NLP	2025-05-26T08:51:22Z	4	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-13T23:13:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e3	ahmedelgebaly	2025-05-26T08:50:39Z	14	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-25T14:03:05Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_e3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used # Load your previously fine-tuned model as a PEFT adapter peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_e3 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e3 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2-v0_SciQ_e3 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e3 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 3 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_e3 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8935 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7866 \| 0.0305 \| 1 \| 1.8420 \| \| 1.1314 \| 0.2443 \| 8 \| 1.0979 \| \| 0.8408 \| 0.4885 \| 16 \| 0.9646 \| \| 0.8669 \| 0.7328 \| 24 \| 0.9339 \| \| 0.8588 \| 0.9771 \| 32 \| 0.9197 \| \| 0.8363 \| 1.2137 \| 40 \| 0.9090 \| \| 0.8021 \| 1.4580 \| 48 \| 0.9028 \| \| 0.833 \| 1.7023 \| 56 \| 0.8995 \| \| 0.8083 \| 1.9466 \| 64 \| 0.8951 \| \| 0.8215 \| 2.1832 \| 72 \| 0.8948 \| \| 0.824 \| 2.4275 \| 80 \| 0.8945 \| \| 0.802 \| 2.6718 \| 88 \| 0.8936 \| \| 0.7762 \| 2.9160 \| 96 \| 0.8935 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
Mihaj/whisper-medium-karelian-cs-w-rus	Mihaj	2025-05-26T08:49:41Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-20T09:38:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e2	ahmedelgebaly	2025-05-26T08:48:32Z	16	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Llama-3.1-8B", "base_model:adapter:meta-llama/Llama-3.1-8B", "license:llama3", "4-bit", "bitsandbytes", "region:us" ]	null	2025-04-25T14:02:55Z	--- library_name: peft license: llama3 base_model: meta-llama/Meta-Llama-3.1-8B tags: - axolotl - generated_from_trainer model-index: - name: llama-3.1-8b-squadv2_SciQ_e2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used # Load your previously fine-tuned model as a PEFT adapter peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_e2 model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer load_in_8bit: false load_in_4bit: true strict: false datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: train test_datasets: - path: ahmedelgebaly/SciQ_Alpaca type: alpaca split: validation dataset_prepared_path: output_dir: ./outputs/qlora-out adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_modules: lora_target_linear: true lora_fan_in_fan_out: wandb_project: llama-3.1-8b-squadv2_SciQ_e2 wandb_entity: wandb_watch: wandb_name: llama-3.1-8b-squadv2-v0_SciQ_e2 wandb_log_model: hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e2 gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 2 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: pad_token: "<\|end_of_text\|>" ``` </details><br> # llama-3.1-8b-squadv2_SciQ_e2 This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9066 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.7866 \| 0.0305 \| 1 \| 1.8420 \| \| 1.1295 \| 0.2443 \| 8 \| 1.0980 \| \| 0.8408 \| 0.4885 \| 16 \| 0.9650 \| \| 0.8677 \| 0.7328 \| 24 \| 0.9346 \| \| 0.8605 \| 0.9771 \| 32 \| 0.9223 \| \| 0.8401 \| 1.2137 \| 40 \| 0.9130 \| \| 0.8089 \| 1.4580 \| 48 \| 0.9084 \| \| 0.8434 \| 1.7023 \| 56 \| 0.9068 \| \| 0.8224 \| 1.9466 \| 64 \| 0.9066 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.45.2 - Pytorch 2.3.1+cu121 - Datasets 3.0.1 - Tokenizers 0.20.1
igzi/MNLP_document_encoder-finetuned	igzi	2025-05-26T08:48:30Z	0	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-05-26T08:48:19Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AK2042/Phishing_Website_detector	AK2042	2025-05-26T08:46:06Z	0	0	sklearn	[ "sklearn", "en", "license:mit", "region:us" ]	null	2025-05-26T07:23:06Z	--- license: mit language: - en library_name: sklearn --- # Phishing Website Detection using Machine Learning & SSL Certificate Analysis This project is a machine learning-based web application to detect phishing websites using both URL-based features and SSL certificate metadata. It uses a trained model and provides an easy-to-use Gradio interface to check whether a given link is legitimate or phishing. --- ## Features * Accepts a raw URL as input * Uses lexical URL features + SSL certificate metadata * Extracts SSL features like issuer, validity period, and self-signed status * Trained ML model (Random Forest / XGBoost / etc.) saved as a `.pkl` file * Gradio web interface (no backend deployment needed) * Fast and lightweight prediction * Built using Kaggle-curated phishing URL dataset --- ## Project Structure ``` phishing-detector/ │ ├── model/ │ └── phishing_model.pkl # Trained ML model │ ├── app.py # Main Gradio app ├── feature_extraction.py # Lexical feature extractor for URLs ├── train_model.py # (Optional) Script to retrain model │ ├── README.md # You are here! └── requirements.txt # Python dependencies ``` --- ## How It Works 1. User inputs a URL. 2. `feature_extraction.py` extracts URL-based features (length, special chars, etc.). 3. Features are fed into a trained ML model (`phishing_model.pkl`). 4. Output shown on Gradio UI: Legit or Phishing --- ## Setup & Run ### 1. Clone the Repository ```bash git clone https://github.com/AK2042/Phishing_Website_detector.git cd phishing-detector ``` ### 2. Install Dependencies ```bash pip install -r requirements.txt ``` ### 3. Run the App ```bash python app.py ``` Gradio will open the app in your browser at `http://127.0.0.1:7860`. --- ## Model Training (Optional) To retrain the model with new data: ```bash python train_model.py ``` This will generate a new `phishing_model.pkl`. link to dataset: https://www.kaggle.com/datasets/eswarchandt/phishing-website-detector ## Dependencies * `scikit-learn` * `gradio` * `OpenSSL` * `tldextract` * `pandas`, `numpy` --- ## References * [PhishTank Dataset](https://www.phishtank.com/) * [Kaggle Phishing URLs Dataset](https://www.kaggle.com/datasets) * [Gradio Docs](https://gradio.app/) --- ## License MIT License. Use freely with credit.
FormlessAI/cf2da658-1b17-4700-b77f-d3e98017d67c	FormlessAI	2025-05-26T08:41:48Z	0	0	transformers	[ "transformers", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:openlm-research/open_llama_3b", "base_model:finetune:openlm-research/open_llama_3b", "endpoints_compatible", "region:us" ]	null	2025-05-26T07:20:10Z	--- base_model: openlm-research/open_llama_3b library_name: transformers model_name: cf2da658-1b17-4700-b77f-d3e98017d67c tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for cf2da658-1b17-4700-b77f-d3e98017d67c This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="FormlessAI/cf2da658-1b17-4700-b77f-d3e98017d67c", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/tq3iv1h1) This model was trained with SFT. ### Framework versions - TRL: 0.17.0 - Transformers: 4.52.3 - Pytorch: 2.7.0+cu128 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
green19d25y/Qwen2-32m-hf	green19d25y	2025-05-26T06:28:07Z	0	0	null	[ "safetensors", "qwen2", "text-generation", "en", "license:mit", "region:us" ]	text-generation	2025-05-26T06:10:38Z	--- license: mit language: - en pipeline_tag: text-generation --- # Qwen2 HF model (32M Parameters) This is a Qwen2 architecture model trained completely from scratch with 32 million parameters. It uses a custom tokenizer and vocabulary, and is designed for experimentation with compact, task-specific language models. ## Training Details - Architecture: Qwen2 - Parameters: 32M - Training from scratch: Yes - Pretrained base: None - Tokenizer: ByteLevelBPETokenizer - Vocabulary size: 5K tokens - Language: English only - Dataset: [Shakespeare's Complete Works](https://www.gutenberg.org/ebooks/100) ## Purpose To check if the Qwen2 works well with small amount of data. It somewhat works, but I believe I need to fine-tune it and perform additional steps to make it more accurate. ## Intended Use - Small-scale research - Testing text generation on limited data - Fine-grained experimentation with custom language models - Educational purposes ## Limitations - Not general-purpose - Limited vocabulary and context length - Struggles outside its trained domain - English-only - Not production-ready ## Inference Example ```python from transformers import Qwen2ForCausalLM, Qwen2Tokenizer model = Qwen2ForCausalLM.from_pretrained("green19d25y/Qwen2-32m-hf") tokenizer = Qwen2Tokenizer.from_pretrained("green19d25y/Qwen2-32m-hf") prompt = "He had need mean better than his" input_ids = tokenizer.encode(prompt, return_tensors="pt") output = model.generate( input_ids, max_length=100, num_return_sequences=1, do_sample=True, temperature=0.7 ) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ```
Lonz1no/Qwen3_Rude_RAG	Lonz1no	2025-05-26T06:27:28Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-05-26T06:27:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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dhruvsangani/Sentiment-Analysis-GGUF	dhruvsangani	2025-05-26T06:25:53Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2025-05-26T06:04:41Z	--- base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: dhruvsangani - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Yuichi1218/Lafeak-llama3-chatvector-05261128	Yuichi1218	2025-05-26T06:23:18Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-05-26T06:15:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Yntec/Luminous	Yntec	2025-05-26T06:21:11Z	42	1	diffusers	[ "diffusers", "safetensors", "General purpose", "3D", "Person", "Colorful", "Stylized", "Artstyle", "Patchmonk", "sadxzero", "stable-diffusion", "stable-diffusion-1.5", "stable-diffusion-diffusers", "text-to-image", "base_model:digiplay/SXZ_Luma_v0.98VAE", "base_model:finetune:digiplay/SXZ_Luma_v0.98VAE", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2025-05-06T06:22:41Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - General purpose - 3D - Person - Colorful - Stylized - Artstyle - Patchmonk - sadxzero - stable-diffusion - stable-diffusion-1.5 - stable-diffusion-diffusers - diffusers - text-to-image base_model: - digiplay/SXZ_Luma_v0.98VAE --- # Luminous LusciousMix V2.5 merged with the SXZ Luma 0.98 model to maximize their creativity! Samples and prompts (all use seed 9119): ![Pink haired girl illustration](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/JDHcqgVYZlAaj0isTVx6n.png) (masterpiece), best quality, high resolution, highly detailed, detailed background, perfect lighting, outdoor, 1girl, petite, short hair, pink hair, blunt bangs, t-shirt, short skirt ![Face of blue eyes blonde illustration](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/a_2NYtl0S5o1dbdqtwdc3.png) photo of an extremely beautiful young girl with blonde hair, ultra realistic blue eyes by annie leibovitz, sundress. hyperdetailed digital concept art trending in pinterest Artstation WLOP 3 point lighting cinematic highlights stunning quality 8k oil on canvas shaded flat illustration for fashion photoshoot ![Ginger pirate in a storm](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/yBawXRplw-3zQYsYy_s5e.png) cute shot of redhead pirate young girl, long green coat, sea, storm, dark atmosphere, volumetric lighting, teal eyes, glad to see, best quality, masterpiece, chromatic aberration, realistic ![Female superman illustration](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/WSsa9IlJ1Ey2PLzATxWtX.png) cute lady in superman costume flying in sky, short black hair, cape, eyes, arms up, storm, dark clouds, lightning, night, lightning, rain, particles Original pages: https://civitai.com/models/25831?modelVersionId=68200 (Luma 0.98) https://civitai.com/models/24354?modelVersionId=188775 (LusciousMix 2.5) Recipe: # Recipe: - SuperMerger Weight sum Use MBW 1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,1,1,1 Model A: Luscious 2.5 Model B: Luma 0.98VAE Output Model: Luminous
ViRAL-Nimra-Mehra-Video-Leaks/Original.Full.Clip.Nimra.Mehra.Viral.Video.Link.Official	ViRAL-Nimra-Mehra-Video-Leaks	2025-05-26T06:19:33Z	0	0	null	[ "region:us" ]	null	2025-05-26T06:19:20Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Cloudmaster/Llama-3.2-3B-8bit-gptq-attention	Cloudmaster	2025-05-26T06:18:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "gptq", "region:us" ]	text-generation	2025-05-26T06:15:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Nerva1228/kxnainai1	Nerva1228	2025-05-26T06:17:29Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-05-26T02:18:36Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: kxnainai1 --- # Kxnainai1 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `kxnainai1` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "kxnainai1", "lora_weights": "https://huggingface.co/Nerva1228/kxnainai1/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Nerva1228/kxnainai1', weight_name='lora.safetensors') image = pipeline('kxnainai1').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 1000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Nerva1228/kxnainai1/discussions) to add images that show off what you’ve made with this LoRA.
lefantom00/Llama-3.1-8B-it-iSMART-v2	lefantom00	2025-05-26T06:15:30Z	0	0	null	[ "safetensors", "llama", "vi", "base_model:meta-llama/Llama-3.1-8B-Instruct", "base_model:finetune:meta-llama/Llama-3.1-8B-Instruct", "license:apache-2.0", "region:us" ]	null	2025-05-26T04:42:15Z	--- license: apache-2.0 language: - vi base_model: - meta-llama/Llama-3.1-8B-Instruct ---
rendoo/06_rendoo_05_972	rendoo	2025-05-26T06:07:18Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "gemma", "google", "Bifröst", "Bifrost", "code", "text-generation", "conversational", "base_model:google/gemma-3-27b-it", "base_model:finetune:google/gemma-3-27b-it", "license:gemma", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T05:57:45Z	--- license: gemma library_name: transformers pipeline_tag: text-generation extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license base_model: google/gemma-3-27b-it tags: - transformers - gemma3 - gemma - google - Bifröst - Bifrost - code --- ## Bifröst-27B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64a834a8895fd6416e29576f/sAXfe0cQdULI_GEVxBstw.png) Bifröst-27B is an advanced AI model built upon gemma3 architecture, specifically fine-tuned for secure and efficient enterprise-grade code generation with reasoning. Designed to meet rigorous standards of safety, accuracy, and reliability, Bifröst empowers organizations to streamline software development workflows while prioritizing security and compliance. ### Model Details - Model Name: Bifröst-27B - Base Architecture: gemma3 - Application: Enterprise Secure Code Generation - Release Date: 16-March-2025 ### Intended Use Bifröst is designed explicitly for: - Generating secure, efficient, and high-quality code. - Supporting development tasks within regulated enterprise environments. - Enhancing productivity by automating routine coding tasks without compromising security. ### Features - Security-Focused Training: Specialized training regimen emphasizing secure coding practices, vulnerability reduction, and adherence to security standards. - Enterprise-Optimized Performance: Tailored to support various programming languages and enterprise frameworks with robust, context-aware suggestions. - Compliance-Driven Design: Incorporates features to aid in maintaining compliance with industry-specific standards (e.g., GDPR, HIPAA, SOC 2). ### Limitations - Bifröst should be used under human supervision to ensure code correctness and security compliance. - Model-generated code should undergo appropriate security and quality assurance checks before deployment. ### Ethical Considerations - Users are encouraged to perform regular audits and compliance checks on generated outputs. - Enterprises should implement responsible AI practices to mitigate biases or unintended consequences. ### Usage Below are some quick-start instructions for using the model with the `transformers` library. #### Installation ```sh $ pip install git+https://github.com/huggingface/[email protected] ``` #### Running with the `pipeline` API ```python from transformers import pipeline import torch pipe = pipeline( "text-generation", model="OpenGenerativeAI/Bifrost-27B", device="cuda", torch_dtype=torch.bfloat16 ) messages = [{"role": "user", "content": "Generate a secure API key management system."}] output = pipe(text=messages, max_new_tokens=200) print(output[0]["generated_text"]) ``` ## Terms of Use This model is released under the Gemma license. Users must comply with [Google's Gemma Terms of Use](https://ai.google.dev/gemma/terms), including restrictions on redistribution, modification, and commercial use.
enosislabs/midnight-mini-high-exp-gguf	enosislabs	2025-05-26T06:05:49Z	82	1	transformers	[ "transformers", "gguf", "llama", "llama-3.2-3b", "unsloth", "midnight-ai", "enosis-labs", "text-generation", "summarization", "mathematics", "psychology", "fine-tuned", "efficient", "daily-use", "trl", "text-generation-inference", "en", "dataset:enosislabs/deepsearch-llama-finetune", "base_model:meta-llama/Llama-3.2-3B", "base_model:quantized:meta-llama/Llama-3.2-3B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-05-23T01:18:48Z	--- license: apache-2.0 language: - en tags: - llama - llama-3.2-3b - unsloth - midnight-ai - enosis-labs - text-generation - summarization - mathematics - psychology - fine-tuned - efficient - daily-use - trl - text-generation-inference - transformers pipeline_tag: text-generation model_name: Midnight Mini Standard model_id: enosislabs/midnight-mini-high-exp base_model: meta-llama/Llama-3.2-3B datasets: - enosislabs/deepsearch-llama-finetune library_name: transformers --- # Midnight Mini Standard: Efficient Daily AI Companion Model ID: `enosislabs/midnight-mini-high-exp` Developed by: Enosis Labs AI Research Division Base Architecture: Llama-3.2-3B License: Apache-2.0 ## Executive Summary Midnight Mini Standard represents our commitment to democratizing AI through efficient, practical solutions for everyday use. Built upon the robust Llama-3.2-3B foundation, this 3-billion parameter model is specifically optimized for daily productivity tasks, delivering exceptional performance in text summarization, basic mathematics, psychology-oriented interactions, and rapid response generation while maintaining minimal computational requirements. ## Technical Specifications ### Core Architecture - Base Model: meta-llama/Llama-3.2-3B - Parameter Count: 3.21 billion trainable parameters - Model Type: Autoregressive Transformer (Causal Language Model) - Fine-tuning Framework: Unsloth optimization pipeline with TRL integration - Quantization Support: Native 16-bit precision, GGUF quantized variants (Q4_K_M, Q5_K_M, Q8_0) - Maximum Context Length: 131,072 tokens (extended context) - Vocabulary Size: 128,256 tokens - Attention Heads: 24 (Multi-Head Attention) - Hidden Dimensions: 2,048 - Feed-Forward Network Dimensions: 8,192 ### Performance Characteristics The model architecture emphasizes efficiency and practical utility: - Optimized Inference Speed: Specialized for rapid response generation in conversational scenarios - Memory Efficient Design: Reduced memory footprint for deployment on consumer hardware - Context-Aware Processing: Enhanced short-term memory for maintaining conversation flow - Task-Specific Optimization: Fine-tuned attention patterns for summarization and mathematical reasoning ### Deployment Formats #### 16-bit Precision Model - Memory Requirements: ~6.5GB VRAM (inference) - Inference Speed: ~200-250 tokens/second (RTX 4070) - Precision: Full fp16 precision for optimal accuracy #### GGUF Quantized Variants - Q4_K_M: 2.1GB, optimal for CPU inference and edge deployment - Q5_K_M: 2.6GB, enhanced quality with efficient compression - Q8_0: 3.4GB, near-original quality for high-performance applications ## Core Capabilities & Optimization Focus Midnight Mini Standard is engineered for practical, everyday AI assistance with specialized capabilities: ### Primary Strengths - Rapid Response Generation: Optimized for quick, coherent responses in conversational contexts - Text Summarization Excellence: Superior performance in condensing complex documents and articles - Basic Mathematical Proficiency: Reliable arithmetic, algebra, and fundamental mathematical operations - Psychology-Informed Interactions: Enhanced understanding of emotional context and supportive communication - Daily Productivity Support: Streamlined assistance for common tasks like email drafting, note-taking, and planning ### Design Philosophy - Efficiency First: Maximized performance per computational unit for practical deployment - User-Centric Design: Optimized for natural, helpful interactions in daily scenarios - Accessibility Focus: Designed to run efficiently on consumer-grade hardware - Reliability: Consistent, dependable outputs for routine tasks ## Specialized Applications & Use Cases Midnight Mini Standard excels in practical, everyday scenarios: ### Primary Application Domains - Personal Productivity: Email composition, document summarization, meeting notes, and task planning - Educational Support: Homework assistance, concept explanation, and basic tutoring across subjects - Content Creation: Blog post drafts, social media content, and creative writing assistance - Psychology & Wellness: Supportive conversations, mood tracking insights, and mental health resource guidance - Business Communication: Professional correspondence, report summarization, and presentation assistance ### Implementation Examples #### Text Summarization Implementation ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Initialize model for summarization tasks model_id = "enosislabs/midnight-mini-standard" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" ) # Document summarization example document = """[Long article or document text here]""" prompt = f"""Please provide a concise summary of the following text, highlighting the key points: {document} Summary:""" inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096) with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=200, temperature=0.3, do_sample=True, top_p=0.9, repetition_penalty=1.1 ) summary = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Summary:\n{summary}") ``` #### Psychology-Informed Interaction ```python # Supportive conversation example support_prompt = """I'm feeling overwhelmed with my workload and struggling to stay motivated. Can you help me develop a strategy to manage this situation?""" inputs = tokenizer(support_prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=300, temperature=0.6, do_sample=True, top_p=0.85 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Supportive Response:\n{response}") ``` #### Basic Mathematics Assistance ```python # Mathematical problem solving math_prompt = """Solve this step by step: If a recipe calls for 2.5 cups of flour to make 12 cookies, how much flour is needed to make 30 cookies?""" inputs = tokenizer(math_prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=150, temperature=0.2, do_sample=True ) solution = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Mathematical Solution:\n{solution}") ``` ## Training Methodology & Data Engineering ### Training Infrastructure - Base Model: meta-llama/Llama-3.2-3B (Meta AI) - Fine-tuning Framework: Unsloth optimization with TRL (Transformer Reinforcement Learning) - Hardware Configuration: Multi-GPU training environment (RTX 4090 clusters) - Training Duration: 48 hours of efficient training with optimized data pipeline - Optimization Strategy: Parameter-efficient fine-tuning with focus on practical task performance ### Dataset Composition & Curation Training incorporates the proprietary `enosislabs/deepsearch-llama-finetune` dataset: - Conversational Data: Natural dialogue patterns optimized for daily interaction scenarios - Summarization Corpus: Diverse documents, articles, and texts with high-quality summaries - Mathematical Problem Sets: Basic to intermediate mathematical problems with step-by-step solutions - Psychology Resources: Mental health support conversations and emotional intelligence training data - Productivity Content: Email templates, professional communication, and task management examples ### Training Optimization Techniques - Efficient Fine-tuning: Leveraging Unsloth's optimized training pipeline for reduced training time - Task-Specific Adaptation: Specialized training loops for different capability areas - Response Quality Enhancement: Reinforcement learning from human feedback (RLHF) integration - Conversational Flow Optimization: Training for natural, engaging dialogue patterns ## Performance Benchmarks & Evaluation Results Midnight Mini Standard demonstrates strong performance in practical application scenarios: ### Benchmark Results Overview \| Capability Area \| Task Specification \| Metric \| Score \| Performance Notes \| \|:----------------\|:-------------------\|:-------\|:------\|:------------------\| \| Text Summarization \| \| \| \| \| \| \| News Article Summarization \| ROUGE-L \| 0.485 \| Excellent content preservation \| \| \| Document Condensation \| Compression Ratio \| 4.2:1 \| Optimal information density \| \| Mathematical Reasoning \| \| \| \| \| \| \| Basic Arithmetic \| Accuracy \| 0.942 \| Reliable for daily calculations \| \| \| Word Problems \| Success Rate \| 0.876 \| Strong practical problem solving \| \| Conversational Quality \| \| \| \| \| \| \| Response Relevance \| Human Rating \| 4.3/5 \| Highly contextual responses \| \| \| Helpfulness Score \| User Evaluation \| 4.5/5 \| Excellent practical assistance \| \| Psychology Applications \| \| \| \| \| \| \| Emotional Recognition \| F1-Score \| 0.821 \| Strong emotional intelligence \| \| \| Supportive Response Quality \| Expert Rating \| 4.2/5 \| Appropriate therapeutic communication \| ### Performance Analysis Summarization Excellence: Achieves industry-leading performance in text summarization with optimal balance between brevity and information retention, making it ideal for processing news, reports, and documentation. Mathematical Reliability: Demonstrates consistent accuracy in basic mathematical operations and word problems, providing reliable assistance for everyday computational needs. Conversational Quality: High user satisfaction ratings indicate natural, helpful interactions that feel genuinely supportive and contextually appropriate. Psychology Applications: Strong emotional recognition capabilities enable empathetic responses suitable for mental health support and wellness applications. ## Model Limitations & Considerations ### Technical Constraints - Knowledge Boundary: Training data limited to cutoff date; requires external sources for current information - Mathematical Scope: Optimized for basic to intermediate mathematics; complex theoretical problems may require specialized models - Context Limitations: While extended to 131K tokens, extremely long documents may need segmentation - Language Focus: Primarily optimized for English with limited multilingual capabilities ### Performance Considerations - Specialized Domain Accuracy: General-purpose design may require domain-specific validation for specialized fields - Creative Writing Limitations: Optimized for practical tasks rather than advanced creative or artistic applications - Technical Depth: Designed for daily use rather than deep technical or research applications - Real-time Information: Cannot access current events or real-time data without external integration ### Ethical & Safety Considerations - Psychology Applications: Not a replacement for professional mental health care; should supplement, not substitute, professional support - Bias Awareness: May reflect training data biases; requires ongoing monitoring in sensitive applications - Decision Making: Intended as an assistant tool; important decisions should involve human judgment - Privacy Protection: No data retention during inference; user conversations are not stored ## Responsible AI Implementation ### Safety Mechanisms - Content Filtering: Integrated safety measures to prevent harmful or inappropriate content generation - Emotional Sensitivity: Training for appropriate responses in sensitive or emotional contexts - Professional Boundaries: Clear limitations in psychology applications to prevent overstepping therapeutic boundaries - User Guidance: Transparent communication about model capabilities and limitations ### Best Practices for Deployment - Supervised Implementation: Recommend human oversight for critical applications - User Education: Clear communication about model strengths and limitations - Feedback Integration: Continuous improvement through user feedback and performance monitoring - Ethical Guidelines: Adherence to responsible AI principles in all applications ## Technical Support & Resources ### Model Attribution When utilizing Midnight Mini Standard in applications or research, please cite: ```bibtex @software{midnight_mini_standard_2025, author = {Enosis Labs AI Research Division}, title = {Midnight Mini Standard: Efficient Daily AI Companion}, year = {2025}, publisher = {Enosis Labs}, url = {https://huggingface.co/enosislabs/midnight-mini-standard}, note = {3B parameter Llama-based model optimized for daily productivity and practical applications} } ``` ### Support Channels For technical support, implementation guidance, or collaboration opportunities: - Primary Contact: <[email protected]> - Model Repository: [Hugging Face Model Hub](https://huggingface.co/enosislabs/midnight-mini-high-exp) ### License & Distribution Licensed under Apache 2.0, enabling broad commercial and personal use with proper attribution. The model is designed for accessibility and widespread adoption in practical AI applications. --- Enosis Labs AI Research Division** Making advanced AI accessible for everyday life
enosislabs/midnight-mini-high-exp	enosislabs	2025-05-26T06:05:25Z	20	1	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "llama-3.2-3b", "unsloth", "midnight-ai", "enosis-labs", "summarization", "mathematics", "psychology", "fine-tuned", "efficient", "daily-use", "trl", "text-generation-inference", "conversational", "en", "dataset:enosislabs/deepsearch-llama-finetune", "base_model:meta-llama/Llama-3.2-3B", "base_model:finetune:meta-llama/Llama-3.2-3B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-05-22T23:56:29Z	--- license: apache-2.0 language: - en tags: - llama - llama-3.2-3b - unsloth - midnight-ai - enosis-labs - text-generation - summarization - mathematics - psychology - fine-tuned - efficient - daily-use - trl - text-generation-inference - transformers pipeline_tag: text-generation model_name: Midnight Mini Standard model_id: enosislabs/midnight-mini-high-exp base_model: meta-llama/Llama-3.2-3B datasets: - enosislabs/deepsearch-llama-finetune library_name: transformers --- # Midnight Mini Standard: Efficient Daily AI Companion Model ID: `enosislabs/midnight-mini-high-exp` Developed by: Enosis Labs AI Research Division Base Architecture: Llama-3.2-3B License: Apache-2.0 ## Executive Summary Midnight Mini Standard represents our commitment to democratizing AI through efficient, practical solutions for everyday use. Built upon the robust Llama-3.2-3B foundation, this 3-billion parameter model is specifically optimized for daily productivity tasks, delivering exceptional performance in text summarization, basic mathematics, psychology-oriented interactions, and rapid response generation while maintaining minimal computational requirements. ## Technical Specifications ### Core Architecture - Base Model: meta-llama/Llama-3.2-3B - Parameter Count: 3.21 billion trainable parameters - Model Type: Autoregressive Transformer (Causal Language Model) - Fine-tuning Framework: Unsloth optimization pipeline with TRL integration - Quantization Support: Native 16-bit precision, GGUF quantized variants (Q4_K_M, Q5_K_M, Q8_0) - Maximum Context Length: 131,072 tokens (extended context) - Vocabulary Size: 128,256 tokens - Attention Heads: 24 (Multi-Head Attention) - Hidden Dimensions: 2,048 - Feed-Forward Network Dimensions: 8,192 ### Performance Characteristics The model architecture emphasizes efficiency and practical utility: - Optimized Inference Speed: Specialized for rapid response generation in conversational scenarios - Memory Efficient Design: Reduced memory footprint for deployment on consumer hardware - Context-Aware Processing: Enhanced short-term memory for maintaining conversation flow - Task-Specific Optimization: Fine-tuned attention patterns for summarization and mathematical reasoning ### Deployment Formats #### 16-bit Precision Model - Memory Requirements: ~6.5GB VRAM (inference) - Inference Speed: ~200-250 tokens/second (RTX 4070) - Precision: Full fp16 precision for optimal accuracy #### GGUF Quantized Variants - Q4_K_M: 2.1GB, optimal for CPU inference and edge deployment - Q5_K_M: 2.6GB, enhanced quality with efficient compression - Q8_0: 3.4GB, near-original quality for high-performance applications ## Core Capabilities & Optimization Focus Midnight Mini Standard is engineered for practical, everyday AI assistance with specialized capabilities: ### Primary Strengths - Rapid Response Generation: Optimized for quick, coherent responses in conversational contexts - Text Summarization Excellence: Superior performance in condensing complex documents and articles - Basic Mathematical Proficiency: Reliable arithmetic, algebra, and fundamental mathematical operations - Psychology-Informed Interactions: Enhanced understanding of emotional context and supportive communication - Daily Productivity Support: Streamlined assistance for common tasks like email drafting, note-taking, and planning ### Design Philosophy - Efficiency First: Maximized performance per computational unit for practical deployment - User-Centric Design: Optimized for natural, helpful interactions in daily scenarios - Accessibility Focus: Designed to run efficiently on consumer-grade hardware - Reliability: Consistent, dependable outputs for routine tasks ## Specialized Applications & Use Cases Midnight Mini Standard excels in practical, everyday scenarios: ### Primary Application Domains - Personal Productivity: Email composition, document summarization, meeting notes, and task planning - Educational Support: Homework assistance, concept explanation, and basic tutoring across subjects - Content Creation: Blog post drafts, social media content, and creative writing assistance - Psychology & Wellness: Supportive conversations, mood tracking insights, and mental health resource guidance - Business Communication: Professional correspondence, report summarization, and presentation assistance ### Implementation Examples #### Text Summarization Implementation ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch # Initialize model for summarization tasks model_id = "enosislabs/midnight-mini-standard" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" ) # Document summarization example document = """[Long article or document text here]""" prompt = f"""Please provide a concise summary of the following text, highlighting the key points: {document} Summary:""" inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096) with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=200, temperature=0.3, do_sample=True, top_p=0.9, repetition_penalty=1.1 ) summary = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Summary:\n{summary}") ``` #### Psychology-Informed Interaction ```python # Supportive conversation example support_prompt = """I'm feeling overwhelmed with my workload and struggling to stay motivated. Can you help me develop a strategy to manage this situation?""" inputs = tokenizer(support_prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=300, temperature=0.6, do_sample=True, top_p=0.85 ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Supportive Response:\n{response}") ``` #### Basic Mathematics Assistance ```python # Mathematical problem solving math_prompt = """Solve this step by step: If a recipe calls for 2.5 cups of flour to make 12 cookies, how much flour is needed to make 30 cookies?""" inputs = tokenizer(math_prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( inputs, max_new_tokens=150, temperature=0.2, do_sample=True ) solution = tokenizer.decode(outputs[0], skip_special_tokens=True) print(f"Mathematical Solution:\n{solution}") ``` ## Training Methodology & Data Engineering ### Training Infrastructure - Base Model: meta-llama/Llama-3.2-3B (Meta AI) - Fine-tuning Framework: Unsloth optimization with TRL (Transformer Reinforcement Learning) - Hardware Configuration: Multi-GPU training environment (RTX 4090 clusters) - Training Duration: 48 hours of efficient training with optimized data pipeline - Optimization Strategy: Parameter-efficient fine-tuning with focus on practical task performance ### Dataset Composition & Curation Training incorporates the proprietary `enosislabs/deepsearch-llama-finetune` dataset: - Conversational Data: Natural dialogue patterns optimized for daily interaction scenarios - Summarization Corpus: Diverse documents, articles, and texts with high-quality summaries - Mathematical Problem Sets: Basic to intermediate mathematical problems with step-by-step solutions - Psychology Resources: Mental health support conversations and emotional intelligence training data - Productivity Content: Email templates, professional communication, and task management examples ### Training Optimization Techniques - Efficient Fine-tuning: Leveraging Unsloth's optimized training pipeline for reduced training time - Task-Specific Adaptation: Specialized training loops for different capability areas - Response Quality Enhancement: Reinforcement learning from human feedback (RLHF) integration - Conversational Flow Optimization: Training for natural, engaging dialogue patterns ## Performance Benchmarks & Evaluation Results Midnight Mini Standard demonstrates strong performance in practical application scenarios: ### Benchmark Results Overview \| Capability Area \| Task Specification \| Metric \| Score \| Performance Notes \| \|:----------------\|:-------------------\|:-------\|:------\|:------------------\| \| Text Summarization \| \| \| \| \| \| \| News Article Summarization \| ROUGE-L \| 0.485 \| Excellent content preservation \| \| \| Document Condensation \| Compression Ratio \| 4.2:1 \| Optimal information density \| \| Mathematical Reasoning \| \| \| \| \| \| \| Basic Arithmetic \| Accuracy \| 0.942 \| Reliable for daily calculations \| \| \| Word Problems \| Success Rate \| 0.876 \| Strong practical problem solving \| \| Conversational Quality \| \| \| \| \| \| \| Response Relevance \| Human Rating \| 4.3/5 \| Highly contextual responses \| \| \| Helpfulness Score \| User Evaluation \| 4.5/5 \| Excellent practical assistance \| \| Psychology Applications \| \| \| \| \| \| \| Emotional Recognition \| F1-Score \| 0.821 \| Strong emotional intelligence \| \| \| Supportive Response Quality \| Expert Rating \| 4.2/5 \| Appropriate therapeutic communication \| ### Performance Analysis Summarization Excellence: Achieves industry-leading performance in text summarization with optimal balance between brevity and information retention, making it ideal for processing news, reports, and documentation. Mathematical Reliability: Demonstrates consistent accuracy in basic mathematical operations and word problems, providing reliable assistance for everyday computational needs. Conversational Quality: High user satisfaction ratings indicate natural, helpful interactions that feel genuinely supportive and contextually appropriate. Psychology Applications: Strong emotional recognition capabilities enable empathetic responses suitable for mental health support and wellness applications. ## Model Limitations & Considerations ### Technical Constraints - Knowledge Boundary: Training data limited to cutoff date; requires external sources for current information - Mathematical Scope: Optimized for basic to intermediate mathematics; complex theoretical problems may require specialized models - Context Limitations: While extended to 131K tokens, extremely long documents may need segmentation - Language Focus: Primarily optimized for English with limited multilingual capabilities ### Performance Considerations - Specialized Domain Accuracy: General-purpose design may require domain-specific validation for specialized fields - Creative Writing Limitations: Optimized for practical tasks rather than advanced creative or artistic applications - Technical Depth: Designed for daily use rather than deep technical or research applications - Real-time Information: Cannot access current events or real-time data without external integration ### Ethical & Safety Considerations - Psychology Applications: Not a replacement for professional mental health care; should supplement, not substitute, professional support - Bias Awareness: May reflect training data biases; requires ongoing monitoring in sensitive applications - Decision Making: Intended as an assistant tool; important decisions should involve human judgment - Privacy Protection: No data retention during inference; user conversations are not stored ## Responsible AI Implementation ### Safety Mechanisms - Content Filtering: Integrated safety measures to prevent harmful or inappropriate content generation - Emotional Sensitivity: Training for appropriate responses in sensitive or emotional contexts - Professional Boundaries: Clear limitations in psychology applications to prevent overstepping therapeutic boundaries - User Guidance: Transparent communication about model capabilities and limitations ### Best Practices for Deployment - Supervised Implementation: Recommend human oversight for critical applications - User Education: Clear communication about model strengths and limitations - Feedback Integration: Continuous improvement through user feedback and performance monitoring - Ethical Guidelines: Adherence to responsible AI principles in all applications ## Technical Support & Resources ### Model Attribution When utilizing Midnight Mini Standard in applications or research, please cite: ```bibtex @software{midnight_mini_standard_2025, author = {Enosis Labs AI Research Division}, title = {Midnight Mini Standard: Efficient Daily AI Companion}, year = {2025}, publisher = {Enosis Labs}, url = {https://huggingface.co/enosislabs/midnight-mini-standard}, note = {3B parameter Llama-based model optimized for daily productivity and practical applications} } ``` ### Support Channels For technical support, implementation guidance, or collaboration opportunities: - Primary Contact: <[email protected]> - Model Repository: [Hugging Face Model Hub](https://huggingface.co/enosislabs/midnight-mini-high-exp) ### License & Distribution Licensed under Apache 2.0, enabling broad commercial and personal use with proper attribution. The model is designed for accessibility and widespread adoption in practical AI applications. --- Enosis Labs AI Research Division** Making advanced AI accessible for everyday life
Wuhall/xlm-roberta-base-cls	Wuhall	2025-05-26T06:03:10Z	0	0	null	[ "safetensors", "xlm-roberta", "zh", "en", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "region:us" ]	null	2025-05-26T05:57:23Z	--- license: mit language: - zh - en base_model: - FacebookAI/xlm-roberta-base --- {"eval_loss": 0.02062925696372986, "eval_accuracy": 0.9971910112359551, "eval_runtime": 9.3475, "eval_samples_per_second": 76.17, "eval_steps_per_second": 4.814, "epoch": 4.0}
TanAlexanderlz/ALL_RGBCROP_ori16F-8B16F-GACWD1	TanAlexanderlz	2025-05-26T06:02:52Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2025-05-26T02:41:43Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer metrics: - accuracy model-index: - name: ALL_RGBCROP_ori16F-8B16F-GACWD results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ALL_RGBCROP_ori16F-8B16F-GACWD This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3803 - Accuracy: 0.8144 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 1440 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.6146 \| 0.0333 \| 48 \| 0.6323 \| 0.6280 \| \| 0.3307 \| 1.0333 \| 96 \| 0.4748 \| 0.7805 \| \| 0.2425 \| 2.0333 \| 144 \| 0.6149 \| 0.7805 \| \| 0.1629 \| 3.0333 \| 192 \| 0.7126 \| 0.7683 \| ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 2.14.4 - Tokenizers 0.21.1
FireRedTeam/FireRedTTS-1S	FireRedTeam	2025-05-26T06:02:47Z	0	2	null	[ "license:apache-2.0", "region:us" ]	null	2025-04-14T05:34:12Z	--- license: apache-2.0 ---
TheDenk/cogvideox-5b-controlnet-hed-v1	TheDenk	2025-05-26T06:02:37Z	12	2	diffusers	[ "diffusers", "safetensors", "cogvideox", "video-generation", "video-to-video", "controlnet", "en", "license:apache-2.0", "region:us" ]	null	2024-10-23T08:32:21Z	--- license: apache-2.0 language: - en tags: - cogvideox - video-generation - video-to-video - controlnet - diffusers pipeline_tag: video-to-video --- # CogvideoX-5b Controlnet Extention <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/63fde49f6315a264aba6a7ed/frns--XYMiWf0mBUI0UMK.mp4"></video> ### (Warning) This is raw version of controlnet. Better version will be published soon. ### How to Clone repo ```bash git clone https://github.com/TheDenk/cogvideox-controlnet.git cd cogvideox-controlnet ``` Create venv ```bash python -m venv venv source venv/bin/activate ``` Install requirements ```bash pip install -r requirements.txt ``` ### Inference examples #### Inference with cli ```bash python -m inference.cli_demo \ --video_path "resources/car.mp4" \ --prompt "The camera follows behind red car. Car is surrounded by a panoramic view of the vast, azure ocean. Seagulls soar overhead, and in the distance, a lighthouse stands sentinel, its beam cutting through the twilight. The scene captures a perfect blend of adventure and serenity, with the car symbolizing freedom on the open sea." \ --controlnet_type "hed" \ --base_model_path THUDM/CogVideoX-5b \ --controlnet_model_path TheDenk/cogvideox-5b-controlnet-hed-v1 ``` #### Inference with Gradio ```bash python -m inference.gradio_web_demo \ --controlnet_type "hed" \ --base_model_path THUDM/CogVideoX-5b \ --controlnet_model_path TheDenk/cogvideox-5b-controlnet-hed-v1 ``` ## Acknowledgements Original code and models [CogVideoX](https://github.com/THUDM/CogVideo/tree/main). ## Contacts <p>Issues should be raised directly in the repository. For professional support and recommendations please <a>[email protected]</a>.</p>
Video-beanne-valerie-dela-cruz/VIDEO.18.Scandal.Video.beanne.valerie.dela.cruz.telegram.beanne.dela.cruz.video	Video-beanne-valerie-dela-cruz	2025-05-26T05:59:04Z	0	0	null	[ "region:us" ]	null	2025-05-26T05:58:02Z	<animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
g-assismoraes/gemma-3-4b-it-fpi-alpha2.0-fromit-var-agnews	g-assismoraes	2025-05-26T05:57:04Z	0	0	transformers	[ "transformers", "safetensors", "gemma3", "image-text-to-text", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-05-26T05:53:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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TingchenFu/coldrl_3k_qwen-2.5-1.5b_04232202	TingchenFu	2025-05-26T05:55:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "en", "dataset:agentica-org/DeepScaleR-Preview-Dataset", "arxiv:2505.14810", "base_model:Qwen/Qwen2.5-1.5B", "base_model:finetune:Qwen/Qwen2.5-1.5B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-26T03:02:55Z	--- library_name: transformers license: apache-2.0 datasets: - agentica-org/DeepScaleR-Preview-Dataset language: - en base_model: - Qwen/Qwen2.5-1.5B --- # Model Card cold-RL for mathematical reasoning in our MathIF project. Github Repository: https://github.com/TingchenFu/MathIF ## Training Details We base our experiments on the DeepScaler dataset, which contains approximately 40k math reasoning samples. The training is conducted using 16 NVIDIA H100 GPUs. For reinforcement learning, we adopt the GRPO framework and use verifiable outcome-based rewards. The model is trained with VeRL framework with most hyper-parameters following the default setting. ## Evaluation We use nucleus sampling (T=1.0, p=0.95) with a maximum generation length of 16,384 tokens for decoding and vLLM engine for efficient inference. ## Citation <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: ``` @article{fu2025scaling, title={Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models}, author={Fu, Tingchen and Gu, Jiawei and Li, Yafu and Qu, Xiaoye and Cheng, Yu}, journal={arXiv preprint arXiv:2505.14810}, year={2025} } ```
beanne-valerie-dela-cruz/Viral-beanne-valerie-dela-cruz-beanne-dela-cruz-viral-video-beanne-valerie-delacruz-telegram	beanne-valerie-dela-cruz	2025-05-26T05:54:51Z	0	0	null	[ "region:us" ]	null	2025-05-26T05:50:41Z	Watch ➤ ➤ ➤ <a href="https://ninjavidzzz.com/tjkhffghbjd"> Click Here To link ([Viral]beanne valerie dela cruz beanne dela cruz viral video beanne valerie delacruz telegram) ➤►DOWNLOAD ➤<a href="https://ninjavidzzz.com/tjkhffghbjd"> Click Here To link ([Viral]beanne valerie dela cruz beanne dela cruz viral video beanne valerie delacruz telegram)
dhintech/marian-id-en-op	dhintech	2025-05-26T05:45:02Z	0	0	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "translation", "indonesian", "english", "fine-tuned", "meeting-translation", "real-time", "optimized", "id", "en", "dataset:ted_talks_iwslt", "base_model:Helsinki-NLP/opus-mt-id-en", "base_model:finetune:Helsinki-NLP/opus-mt-id-en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2025-05-26T05:12:22Z	--- language: - id - en license: apache-2.0 base_model: Helsinki-NLP/opus-mt-id-en tags: - translation - indonesian - english - marian - fine-tuned - meeting-translation - real-time - optimized pipeline_tag: translation datasets: - ted_talks_iwslt library_name: transformers metrics: - bleu - rouge widget: - text: "Selamat pagi, mari kita mulai rapat hari ini." example_title: "Meeting Start" - text: "Apakah ada pertanyaan mengenai proposal ini?" example_title: "Q&A Session" - text: "Tim marketing akan bertanggung jawab untuk strategi ini." example_title: "Task Assignment" - text: "Teknologi artificial intelligence berkembang sangat pesat di Indonesia." example_title: "Technology Discussion" - text: "Mari kita diskusikan hasil penelitian dan implementasinya." example_title: "Research Discussion" --- # MarianMT Indonesian-English Translation (Optimized for Real-Time Meetings) This model is an optimized fine-tuned version of [Helsinki-NLP/opus-mt-id-en](https://huggingface.co/Helsinki-NLP/opus-mt-id-en) specifically designed for real-time meeting translation from Indonesian to English. ## 🎯 Model Highlights - Optimized for Speed: < 1.0s translation time per sentence - Meeting-Focused: Fine-tuned on business and meeting contexts - High Performance: Improved BLEU score compared to base model - Production Ready: Optimized for real-time applications - Memory Efficient: Reduced model complexity without quality loss ## 📊 Performance Metrics \| Metric \| Base Model \| This Model \| Improvement \| \|--------\|------------\|------------\|-------------\| \| BLEU Score \| 0.388 \| 0.413 \| +6.4% \| \| Translation Speed \| 1.08s \| 0.85s \| 21% faster \| \| ROUGE-1 \| 0.807 \| 0.825 \| +2.2% \| \| Memory Usage \| Standard \| Optimized \| 15% reduction \| ## 🚀 Model Details - Base Model: Helsinki-NLP/opus-mt-id-en - Fine-tuned Dataset: TED Talks parallel corpus (Indonesian-English) - Training Strategy: Optimized fine-tuning with layer freezing - Specialization: Business meetings, presentations, and formal conversations - Training Date: 2025-05-26 - Languages: Indonesian (id) → English (en) - License: Apache 2.0 ## ⚙️ Training Configuration ### Optimized Hyperparameters - Learning Rate: 5e-6 (ultra-low for stable fine-tuning) - Weight Decay: 0.001 (optimal regularization) - Gradient Clipping: 0.5 (conservative clipping) - Dataset Usage: 30% of full dataset (quality over quantity) - Max Sequence Length: 96 tokens (speed optimized) - Training Epochs: 8 - Batch Size: 4 (GPU) / 2 (CPU) - Scheduler: Cosine Annealing with Warm Restarts ### Architecture Optimizations - Layer Freezing: Early encoder layers frozen to preserve base knowledge - Parameter Efficiency: 85-90% of parameters actively trained - Memory Optimization: Gradient accumulation and pin memory - Early Stopping: Patience of 5 epochs to prevent overfitting ## 🛠️ Usage ### Basic Usage ```python from transformers import MarianMTModel, MarianTokenizer # Load model and tokenizer model_name = "dhintech/marian-id-en-op" tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) # Translate Indonesian to English def translate(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=96) outputs = model.generate( inputs, max_length=96, num_beams=3, # Optimized for speed early_stopping=True, do_sample=False ) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example usage indonesian_text = "Selamat pagi, mari kita mulai rapat hari ini." english_translation = translate(indonesian_text) print(english_translation) # Output: "Good morning, let's start today's meeting." ``` ### Optimized Production Usage ```python import time from transformers import MarianMTModel, MarianTokenizer import torch class OptimizedMeetingTranslator: def __init__(self, model_name="dhintech/marian-id-en-op"): self.tokenizer = MarianTokenizer.from_pretrained(model_name) self.model = MarianMTModel.from_pretrained(model_name) # Optimize for inference self.model.eval() if torch.cuda.is_available(): self.model = self.model.cuda() def translate(self, text, max_length=96): start_time = time.time() inputs = self.tokenizer( text, return_tensors="pt", padding=True, truncation=True, max_length=max_length ) if torch.cuda.is_available(): inputs = {k: v.cuda() for k, v in inputs.items()} with torch.no_grad(): outputs = self.model.generate( inputs, max_length=max_length, num_beams=3, early_stopping=True, do_sample=False, pad_token_id=self.tokenizer.pad_token_id ) translation = self.tokenizer.decode(outputs[0], skip_special_tokens=True) translation_time = time.time() - start_time return { 'translation': translation, 'time': translation_time, 'input_length': len(text.split()), 'output_length': len(translation.split()) } # Usage example translator = OptimizedMeetingTranslator() result = translator.translate("Apakah ada pertanyaan mengenai proposal ini?") print(f"Translation: {result['translation']}") print(f"Time: {result['time']:.3f}s") ``` ### Batch Translation for Multiple Sentences ```python def batch_translate(sentences, translator): results = [] total_time = 0 for sentence in sentences: result = translator.translate(sentence) results.append(result) total_time += result['time'] return { 'results': results, 'total_time': total_time, 'average_time': total_time / len(sentences), 'sentences_per_second': len(sentences) / total_time } # Example batch translation meeting_sentences = [ "Selamat pagi, mari kita mulai rapat hari ini.", "Apakah ada pertanyaan mengenai proposal ini?", "Tim marketing akan bertanggung jawab untuk strategi ini.", "Mari kita diskusikan timeline implementasi project ini." ] batch_results = batch_translate(meeting_sentences, translator) print(f"Average translation time: {batch_results['average_time']:.3f}s") print(f"Throughput: {batch_results['sentences_per_second']:.1f} sentences/second") ``` ## 📝 Example Translations ### Business Meeting Context \| Indonesian \| English \| Context \| \|------------\|---------\|---------\| \| Selamat pagi, mari kita mulai rapat hari ini. \| Good morning, let's start today's meeting. \| Meeting Opening \| \| Apakah ada pertanyaan mengenai proposal ini? \| Are there any questions about this proposal? \| Q&A Session \| \| Tim marketing akan bertanggung jawab untuk strategi ini. \| The marketing team will be responsible for this strategy. \| Task Assignment \| \| Mari kita diskusikan timeline implementasi project ini. \| Let's discuss the implementation timeline for this project. \| Project Planning \| \| Terima kasih atas presentasi yang sangat informatif. \| Thank you for the very informative presentation. \| Appreciation \| ### Technical Discussion Context \| Indonesian \| English \| Context \| \|------------\|---------\|---------\| \| Teknologi AI berkembang sangat pesat di Indonesia. \| AI technology is developing very rapidly in Indonesia. \| Tech Discussion \| \| Mari kita analisis data performa bulan lalu. \| Let's analyze last month's performance data. \| Data Analysis \| \| Sistem ini memerlukan optimisasi untuk meningkatkan efisiensi. \| This system needs optimization to improve efficiency. \| Technical Review \| ## 🎯 Intended Use Cases - Real-time Meeting Translation: Live translation during business meetings - Presentation Support: Translating Indonesian presentations to English - Business Communication: Formal business correspondence translation - Educational Content: Academic and educational material translation - Conference Interpretation: Supporting multilingual conferences ## ⚡ Performance Optimizations ### Speed Optimizations - Reduced Beam Search: 3 beams (vs 4-5 in base model) - Early Stopping: Faster convergence - Optimized Sequence Length: 96 tokens maximum - Memory Pinning: Faster GPU transfers - Model Quantization Ready: Compatible with INT8 quantization ### Quality Optimizations - Meeting-Specific Vocabulary: Enhanced business and technical terms - Context Preservation: Better handling of meeting contexts - Formal Register: Optimized for formal Indonesian language - Consistent Terminology: Business-specific term consistency ## 🔧 Technical Specifications - Model Architecture: MarianMT (Transformer-based) - Parameters: ~74M (optimized subset of base model) - Vocabulary Size: 65,000 tokens - Max Input Length: 96 tokens - Max Output Length: 96 tokens - Inference Time: < 1.0s per sentence (GPU) - Memory Requirements: - GPU: 2GB VRAM minimum - CPU: 4GB RAM minimum - Supported Frameworks: PyTorch, ONNX (convertible) ## 📊 Evaluation Results ### Automatic Metrics - BLEU Score: 41.3 (vs 38.8 baseline) - ROUGE-1: 82.5 (vs 80.7 baseline) - ROUGE-2: 71.2 (vs 69.1 baseline) - ROUGE-L: 78.9 (vs 76.5 baseline) - METEOR: 0.742 (vs 0.718 baseline) ### Human Evaluation (Sample: 500 sentences) - Fluency: 4.2/5.0 (vs 3.9 baseline) - Adequacy: 4.1/5.0 (vs 3.8 baseline) - Meeting Context Appropriateness: 4.3/5.0 ## 🚨 Limitations and Considerations - Domain Specificity: Optimized for formal business/meeting contexts - Informal Language: May not perform as well on very casual Indonesian - Regional Dialects: Trained primarily on standard Indonesian - Long Sequences: Performance may degrade for very long sentences (>96 tokens) - Cultural Context: Some cultural nuances may be lost in translation ## 🔄 Model Updates - v1.0.0: Initial release with basic fine-tuning - v1.0.1: Current version with optimized training and speed improvements ## 📚 Citation ```bibtex @misc{marian-id-en-optimized-2025, title={MarianMT Indonesian-English Translation (Optimized for Real-Time Meetings)}, author={DhinTech}, year={2025}, publisher={Hugging Face}, journal={Hugging Face Model Hub}, howpublished={\url{https://huggingface.co/dhintech/marian-id-en-op}}, note={Fine-tuned on TED Talks corpus with meeting-specific optimizations} } ``` ## 🤝 Contributing We welcome contributions to improve this model: - Issue Reports: Please report any translation issues or bugs - Performance Feedback: Share your experience with real-world usage - Dataset Contributions: Help improve the model with more meeting-specific data ## 📞 Contact & Support - Repository: [GitHub Repository](https://github.com/dhintech) - Issues: Report issues through Hugging Face model page - Community: Join discussions in the community tab ## 🙏 Acknowledgments - Base Model: Helsinki-NLP team for the original opus-mt-id-en model - Dataset: TED Talks IWSLT dataset contributors - Framework: Hugging Face Transformers team - Infrastructure: Google Colab for training infrastructure --- This model is specifically optimized for Indonesian business meeting translation scenarios. For general-purpose translation, consider using the base Helsinki-NLP/opus-mt-id-en model.
JSlin/Reinforce-PLE-v0	JSlin	2025-05-26T05:42:52Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2025-05-26T05:42:00Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 16.30 +/- 16.52 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
sergioalves/f86cb2ec-e103-411a-ba2c-c2653861632d	sergioalves	2025-05-26T05:42:42Z	0	0	peft	[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:openlm-research/open_llama_3b", "base_model:adapter:openlm-research/open_llama_3b", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2025-05-26T05:20:08Z	--- library_name: peft license: apache-2.0 base_model: openlm-research/open_llama_3b tags: - axolotl - generated_from_trainer model-index: - name: f86cb2ec-e103-411a-ba2c-c2653861632d results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml absolute_data_files: false adapter: lora base_model: openlm-research/open_llama_3b bf16: true chat_template: llama3 dataset_prepared_path: /workspace/axolotl datasets: - data_files: - 96e6850db6b7c2ae_train_data.json ds_type: json format: custom path: /workspace/input_data/ type: field_instruction: instruct field_output: output format: '{instruction}' no_input_format: '{instruction}' system_format: '{system}' system_prompt: '' debug: null deepspeed: null dpo: beta: 0.1 enabled: true group_by_length: false rank_loss: true reference_model: null early_stopping_patience: null eval_max_new_tokens: 128 eval_table_size: null evals_per_epoch: 1 flash_attention: true fp16: null fsdp: null fsdp_config: null gradient_accumulation_steps: 3 gradient_checkpointing: true gradient_clipping: 1.0 group_by_length: false hub_model_id: sergioalves/f86cb2ec-e103-411a-ba2c-c2653861632d hub_repo: null hub_strategy: end hub_token: null learning_rate: 2.0e-06 load_in_4bit: true load_in_8bit: false local_rank: null logging_steps: 1 lora_alpha: 64 lora_dropout: 0.1 lora_fan_in_fan_out: null lora_model_dir: null lora_r: 32 lora_target_linear: true lr_scheduler: cosine max_steps: 500 micro_batch_size: 6 mixed_precision: bf16 mlflow_experiment_name: /tmp/96e6850db6b7c2ae_train_data.json model_type: AutoModelForCausalLM num_epochs: 2 optimizer: adamw_bnb_8bit output_dir: miner_id_24 pad_to_sequence_len: true resume_from_checkpoint: null s2_attention: null sample_packing: false saves_per_epoch: 1 sequence_len: 1024 special_tokens: pad_token: </s> strict: false tf32: false tokenizer_type: AutoTokenizer train_on_inputs: false trust_remote_code: true val_set_size: 0.05 wandb_entity: null wandb_mode: online wandb_name: 3e11bdac-af00-4520-84f7-df6ea744d307 wandb_project: s56-7 wandb_run: your_name wandb_runid: 3e11bdac-af00-4520-84f7-df6ea744d307 warmup_steps: 50 weight_decay: 0.02 xformers_attention: true ``` </details><br> # f86cb2ec-e103-411a-ba2c-c2653861632d This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7464 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - train_batch_size: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 18 - optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.8532 \| 0.0001 \| 1 \| 1.9880 \| \| 1.8513 \| 0.0372 \| 250 \| 1.8110 \| \| 1.7937 \| 0.0744 \| 500 \| 1.7464 \| ### Framework versions - PEFT 0.13.2 - Transformers 4.46.0 - Pytorch 2.5.0+cu124 - Datasets 3.0.1 - Tokenizers 0.20.1

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